Academic literature on the topic 'Deep supervised learning'
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Journal articles on the topic "Deep supervised learning"
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Kim, Taeheon, Jaewon Hur, and Youkyung Han. "Very High-Resolution Satellite Image Registration Based on Self-supervised Deep Learning." Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography 41, no. 4 (2023): 217–25. http://dx.doi.org/10.7848/ksgpc.2023.41.4.217.
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AlZuhair, Mona Suliman, Mohamed Maher Ben Ismail, and Ouiem Bchir. "Soft Semi-Supervised Deep Learning-Based Clustering." Applied Sciences 13, no. 17 (2023): 9673. http://dx.doi.org/10.3390/app13179673.
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Semi-supervised clustering typically relies on both labeled and unlabeled data to guide the learning process towards the optimal data partition and to prevent falling into local minima. However, researchers’ efforts made to improve existing semi-supervised clustering approaches are relatively scarce compared to the contributions made to enhance the state-of-the-art fully unsupervised clustering approaches. In this paper, we propose a novel semi-supervised deep clustering approach, named Soft Constrained Deep Clustering (SC-DEC), that aims to address the limitations exhibited by existing semi-supervised clustering approaches. Specifically, the proposed approach leverages a deep neural network architecture and generates fuzzy membership degrees that better reflect the true partition of the data. In particular, the proposed approach uses side-information and formulates it as a set of soft pairwise constraints to supervise the machine learning process. This supervision information is expressed using rather relaxed constraints named “should-link” constraints. Such constraints determine whether the pairs of data instances should be assigned to the same or different cluster(s). In fact, the clustering task was formulated as an optimization problem via the minimization of a novel objective function. Moreover, the proposed approach’s performance was assessed via extensive experiments using benchmark datasets. Furthermore, the proposed approach was compared to relevant state-of-the-art clustering algorithms, and the obtained results demonstrate the impact of using minimal previous knowledge about the data in improving the overall clustering performance.
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Wei, Xiang, Xiaotao Wei, Xiangyuan Kong, Siyang Lu, Weiwei Xing, and Wei Lu. "FMixCutMatch for semi-supervised deep learning." Neural Networks 133 (January 2021): 166–76. http://dx.doi.org/10.1016/j.neunet.2020.10.018.
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Zhou, Shusen, Hailin Zou, Chanjuan Liu, Mujun Zang, Zhiwang Zhang, and Jun Yue. "Deep extractive networks for supervised learning." Optik 127, no. 20 (2016): 9008–19. http://dx.doi.org/10.1016/j.ijleo.2016.07.007.
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Fong, A. C. M., and G. Hong. "Boosted Supervised Intensional Learning Supported by Unsupervised Learning." International Journal of Machine Learning and Computing 11, no. 2 (2021): 98–102. http://dx.doi.org/10.18178/ijmlc.2021.11.2.1020.
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Traditionally, supervised machine learning (ML) algorithms rely heavily on large sets of annotated data. This is especially true for deep learning (DL) neural networks, which need huge annotated data sets for good performance. However, large volumes of annotated data are not always readily available. In addition, some of the best performing ML and DL algorithms lack explainability – it is often difficult even for domain experts to interpret the results. This is an important consideration especially in safety-critical applications, such as AI-assisted medical endeavors, in which a DL’s failure mode is not well understood. This lack of explainability also increases the risk of malicious attacks by adversarial actors because these actions can become obscured in the decision-making process that lacks transparency. This paper describes an intensional learning approach which uses boosting to enhance prediction performance while minimizing reliance on availability of annotated data. The intensional information is derived from an unsupervised learning preprocessing step involving clustering. Preliminary evaluation on the MNIST data set has shown encouraging results. Specifically, using the proposed approach, it is now possible to achieve similar accuracy result as extensional learning alone while using only a small fraction of the original training data set.
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Hu, Yu, and Hongmin Cai. "Hypergraph-Supervised Deep Subspace Clustering." Mathematics 9, no. 24 (2021): 3259. http://dx.doi.org/10.3390/math9243259.
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Auto-encoder (AE)-based deep subspace clustering (DSC) methods aim to partition high-dimensional data into underlying clusters, where each cluster corresponds to a subspace. As a standard module in current AE-based DSC, the self-reconstruction cost plays an essential role in regularizing the feature learning. However, the self-reconstruction adversely affects the discriminative feature learning of AE, thereby hampering the downstream subspace clustering. To address this issue, we propose a hypergraph-supervised reconstruction to replace the self-reconstruction. Specifically, instead of enforcing the decoder in the AE to merely reconstruct samples themselves, the hypergraph-supervised reconstruction encourages reconstructing samples according to their high-order neighborhood relations. By the back-propagation training, the hypergraph-supervised reconstruction cost enables the deep AE to capture the high-order structure information among samples, facilitating the discriminative feature learning and, thus, alleviating the adverse effect of the self-reconstruction cost. Compared to current DSC methods, relying on the self-reconstruction, our method has achieved consistent performance improvement on benchmark high-dimensional datasets.
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Fu, Zheren, Yan Li, Zhendong Mao, Quan Wang, and Yongdong Zhang. "Deep Metric Learning with Self-Supervised Ranking." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (2021): 1370–78. http://dx.doi.org/10.1609/aaai.v35i2.16226.
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Deep metric learning aims to learn a deep embedding space, where similar objects are pushed towards together and different objects are repelled against. Existing approaches typically use inter-class characteristics, e.g. class-level information or instance-level similarity, to obtain semantic relevance of data points and get a large margin between different classes in the embedding space. However, the intra-class characteristics, e.g. local manifold structure or relative relationship within the same class, are usually overlooked in the learning process. Hence the data structure cannot be fully exploited and the output embeddings have limitation in retrieval. More importantly, retrieval results lack in a good ranking. This paper presents a novel self-supervised ranking auxiliary framework, which captures intra-class characteristics as well as inter-class characteristics for better metric learning. Our method defines specific transform functions to simulates the local structure change of intra-class in the initial image domain, and formulates a self-supervised learning procedure to fully exploit this property and preserve it in the embedding space. Extensive experiments on three standard benchmarks show that our method significantly improves and outperforms the state-of-the-art methods on the performances of both retrieval and ranking by 2%-4%.
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Dutta, Ujjal Kr, Mehrtash Harandi, and C. Chandra Shekhar. "Semi-Supervised Metric Learning: A Deep Resurrection." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (2021): 7279–87. http://dx.doi.org/10.1609/aaai.v35i8.16894.
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Distance Metric Learning (DML) seeks to learn a discriminative embedding where similar examples are closer, and dissimilar examples are apart. In this paper, we address the problem of Semi-Supervised DML (SSDML) that tries to learn a metric using a few labeled examples, and abundantly available unlabeled examples. SSDML is important because it is infeasible to manually annotate all the examples present in a large dataset. Surprisingly, with the exception of a few classical approaches that learn a linear Mahalanobis metric, SSDML has not been studied in the recent years, and lacks approaches in the deep SSDML scenario. In this paper, we address this challenging problem, and revamp SSDML with respect to deep learning. In particular, we propose a stochastic, graph-based approach that first propagates the affinities between the pairs of examples from labeled data, to that of the unlabeled pairs. The propagated affinities are used to mine triplet based constraints for metric learning. We impose orthogonality constraint on the metric parameters, as it leads to a better performance by avoiding a model collapse.
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Bharati, Aparna, Richa Singh, Mayank Vatsa, and Kevin W. Bowyer. "Detecting Facial Retouching Using Supervised Deep Learning." IEEE Transactions on Information Forensics and Security 11, no. 9 (2016): 1903–13. http://dx.doi.org/10.1109/tifs.2016.2561898.
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Mathilde Caron. "Self-supervised learning of deep visual representations." Bulletin 1024, no. 21 (April 2023): 171–72. http://dx.doi.org/10.48556/sif.1024.21.171.
Full textDissertations / Theses on the topic "Deep supervised learning"
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Tran, Khanh-Hung. "Semi-supervised dictionary learning and Semi-supervised deep neural network." Thesis, université Paris-Saclay, 2021. http://www.theses.fr/2021UPASP014.
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Depuis les années 2010, l’apprentissage automatique (ML) est l’un des sujets qui retient beaucoup l'attention des chercheurs scientifiques. De nombreux modèles de ML ont démontré leur capacité produire d’excellent résultats dans des divers domaines comme Vision par ordinateur, Traitement automatique des langues, Robotique… Toutefois, la plupart de ces modèles emploient l’apprentissage supervisé, qui requiert d’un massive annotation. Par conséquent, l’objectif de cette thèse est d’étudier et de proposer des approches semi-supervisées qui ont plusieurs avantages par rapport à l’apprentissage supervisé. Au lieu d’appliquer directement un classificateur semi-supervisé sur la représentation originale des données, nous utilisons plutôt des types de modèle qui intègrent une phase de l’apprentissage de représentation avant de la phase de classification, pour mieux s'adapter à la non linéarité des données. Dans le premier temps, nous revisitons des outils qui permettent de construire notre modèles semi-supervisés. Tout d’abord, nous présentons deux types de modèle qui possèdent l’apprentissage de représentation dans leur architecture : l’apprentissage de dictionnaire et le réseau de neurones, ainsi que les méthodes d’optimisation pour chaque type de model, en plus, dans le cas de réseau de neurones, nous précisons le problème avec les exemples contradictoires. Ensuite, nous présentons les techniques qui accompagnent souvent avec l’apprentissage semi-supervisé comme l’apprentissage de variétés et le pseudo-étiquetage. Dans le deuxième temps, nous travaillons sur l’apprentissage de dictionnaire. Nous synthétisons en général trois étapes pour construire un modèle semi-supervisée à partir d’un modèle supervisé. Ensuite, nous proposons notre modèle semi-supervisée pour traiter le problème de classification typiquement dans le cas d’un faible nombre d’échantillons d’entrainement (y compris tous labellisés et non labellisés échantillons). D'une part, nous appliquons la préservation de la structure de données de l’espace original à l’espace de code parcimonieux (l’apprentissage de variétés), ce qui est considéré comme la régularisation pour les codes parcimonieux. D'autre part, nous intégrons un classificateur semi-supervisé dans l’espace de code parcimonieux. En outre, nous effectuons le codage parcimonieux pour les échantillons de test en prenant en compte aussi la préservation de la structure de données. Cette méthode apporte une amélioration sur le taux de précision par rapport à des méthodes existantes. Dans le troisième temps, nous travaillons sur le réseau de neurones. Nous proposons une approche qui s’appelle "manifold attack" qui permets de renforcer l’apprentissage de variétés. Cette approche est inspirée par l’apprentissage antagoniste : trouver des points virtuels qui perturbent la fonction de coût sur l’apprentissage de variétés (en la maximisant) en fixant les paramètres du modèle; ensuite, les paramètres du modèle sont mis à jour, en minimisant cette fonction de coût et en fixant les points virtuels. Nous fournissons aussi des critères pour limiter l’espace auquel les points virtuels appartiennent et la méthode pour les initialiser. Cette approche apporte non seulement une amélioration sur le taux de précision mais aussi une grande robustesse contre les exemples contradictoires. Enfin, nous analysons des similarités et des différences, ainsi que des avantages et inconvénients entre l’apprentissage de dictionnaire et le réseau de neurones. Nous proposons quelques perspectives sur ces deux types de modèle. Dans le cas de l’apprentissage de dictionnaire semi-supervisé, nous proposons quelques techniques en inspirant par le réseau de neurones. Quant au réseau de neurones, nous proposons d’intégrer "manifold attack" sur les modèles génératifs<br>Since the 2010's, machine learning (ML) has been one of the topics that attract a lot of attention from scientific researchers. Many ML models have been demonstrated their ability to produce excellent results in various fields such as Computer Vision, Natural Language Processing, Robotics... However, most of these models use supervised learning, which requires a massive annotation. Therefore, the objective of this thesis is to study and to propose semi-supervised learning approaches that have many advantages over supervised learning. Instead of directly applying a semi-supervised classifier on the original representation of data, we rather use models that integrate a representation learning stage before the classification stage, to better adapt to the non-linearity of the data. In the first step, we revisit tools that allow us to build our semi-supervised models. First, we present two types of model that possess representation learning in their architecture: dictionary learning and neural network, as well as the optimization methods for each type of model. Moreover, in the case of neural network, we specify the problem with adversarial examples. Then, we present the techniques that often accompany with semi-supervised learning such as variety learning and pseudo-labeling. In the second part, we work on dictionary learning. We synthesize generally three steps to build a semi-supervised model from a supervised model. Then, we propose our semi-supervised model to deal with the classification problem typically in the case of a low number of training samples (including both labelled and non-labelled samples). On the one hand, we apply the preservation of the data structure from the original space to the sparse code space (manifold learning), which is considered as regularization for sparse codes. On the other hand, we integrate a semi-supervised classifier in the sparse code space. In addition, we perform sparse coding for test samples by taking into account also the preservation of the data structure. This method provides an improvement on the accuracy rate compared to other existing methods. In the third step, we work on neural network models. We propose an approach called "manifold attack" which allows reinforcing manifold learning. This approach is inspired from adversarial learning : finding virtual points that disrupt the cost function on manifold learning (by maximizing it) while fixing the model parameters; then the model parameters are updated by minimizing this cost function while fixing these virtual points. We also provide criteria for limiting the space to which the virtual points belong and the method for initializing them. This approach provides not only an improvement on the accuracy rate but also a significant robustness to adversarial examples. Finally, we analyze the similarities and differences, as well as the advantages and disadvantages between dictionary learning and neural network models. We propose some perspectives on both two types of models. In the case of semi-supervised dictionary learning, we propose some techniques inspired by the neural network models. As for the neural network, we propose to integrate manifold attack on generative models
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Roychowdhury, Soumali. "Supervised and Semi-Supervised Learning in Vision using Deep Neural Networks." Thesis, IMT Alti Studi Lucca, 2019. http://e-theses.imtlucca.it/273/1/Roychowdhury_phdthesis.pdf.
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Deep learning has been a huge success in different vision
tasks like classification, object detection, segmentation etc.,
allowing to start from raw pixels to integrated deep neural models. This thesis aims to solve some of these vision
problems using several deep neural network architectures
in different ways. The first and the core part of this thesis
focuses on a learning framework that extends the previous
work on Semantic Based Regularization (SBR) to integrate
prior knowledge into deep learners. Deep neural networks
are empirical learners and therefore heavily depend on labeled examples, whereas knowledge based learners on the
other hand are not very efficient in solving complex vision
problems. Therefore, SBR is designed as a semi-supervised
framework that can tightly integrate empirical learners with
any available background knowledge to get the advantages
of learning from both perception and reasoning/knowledge.
The framework is learner agnostic and any learning machinery can be used. In the earlier works of SBR, kernel machines
or shallow networks were used as learners. The approach
of the problem, concept of using multi-task logic functions
are borrowed form the previous works of SBR. But for the
first time, in this research work, the integration of logic constraints is done with deep neural networks. The thesis defines a novel back propagation schema for optimization of
deep neural networks in SBR and also uses several heuristics to integrate convex and concave logic constraints into
the deep learners. It also focuses on extensive experimental evaluations performed on multiple image classification
datasets to show how the integration of the prior knowledge
in deep learners can be used to boost the accuracy of several neural architectures over their individual counterparts.
SBR is also used in a video classification problem to automatically annotate surgical and non-surgical tools from videos of
cataracts surgery. This framework achieves a high accuracy compared to the human annotators and the state-of-the-art
DResSys by enforcing temporal consistency among the consecutive video frames using prior knowledge in deep neural networks through collective classification during the inference time. DResSys, an ensemble of deep convolutional
neural networks and a Markov Random Field based framework (CNN-MRF) is used, whereas SBR replaces the MRF
graph with logical constraints for enforcing a regularization
in the temporal domain. Therefore, SBR and DResSys, two
deep learning based frameworks discussed in this thesis, are
able to distill prior knowledge into deep neural networks and
hence become useful tools for decision support during interoperative cataract surgeries, in report generation, in surgical training etc. Therefore, the first part of the thesis designs scientific frameworks that enable exploiting the wealth
of domain knowledge and integrate it with deep convolutional neural networks for solving many real world vision
problems and can be used in several industrial applications.
In the present world, a range of different businesses possess huge databases with visuals which are difficult to manage and make use of. Since they may not have an effective
method to make sense of all the visual data, it might end up
uncategorized and useless. If a visual database does not contain meta data about the images or videos, categorizing it, is
a huge hassle. Classification of images and videos through
useful domain information using these unified frameworks
like SBR is a key solution. The second part of the thesis focuses on another vision problem of image segmentation and
this part of the thesis is more application-specific. However,
it can still be viewed as utilizing some universal and basic
domain knowledge techniques with deep learning models.
It designs two deep learning based frameworks and makes
a head to head comparison of the two approaches in terms
of speed, efficiency and cost. The frameworks are built for
automatic segmentation and classification of contaminants
for cleanliness analysis in automobile, aerospace or manufacturing industries. The frameworks are designed to meet the
foremost industry requirement of having an end-to-end solution that is cheap, reliable, fast and accurate in comparison
to the traditional techniques presently used in the contaminant analysis and quality control process. These end-to-end
solutions when integrated with the simple optical acquisition
systems, will help in replacing the expensive slow systems
presently existing in the market.
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Geiler, Louis. "Deep learning for churn prediction." Electronic Thesis or Diss., Université Paris Cité, 2022. http://www.theses.fr/2022UNIP7333.
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Le problème de la prédiction de l’attrition est généralement réservé aux équipes de marketing. Cependant,grâce aux avancées technologiques, de plus en plus de données peuvent être collectés afin d’analyser le comportement des clients. C’est dans ce cadre que cette thèse s’inscrit, plus particulièrement par l’exploitation des méthodes d’apprentissages automatiques. Ainsi, nous avons commencés par étudier ce problème dans le cadre de l’apprentissage supervisé. Nous avons montré que la combinaison en ensemble de la régression logistique, des forêt aléatoire et de XGBoost offraient les meilleurs résultats en terme d’Aire sous la courbe (Are Under the Curve, AUC). Nous avons également montré que les méthodes du type ré-échantillonage jouent uniquement un rôle local et non pas global.Ensuite, nous avons enrichi nos prédictions en prenant en compte la segmentation des clients. En effet, certains clients peuvent quitter le service à cause d’un coût qu’ils jugent trop élevés ou suite à des difficultés rencontrés avec le service client. Notre approche a été réalisée avec une nouvelle architecture de réseaux de neurones profonds qui exploite à la fois les autoencodeur et l’approche desk-means. De plus, nous nous sommes intéressés à l’apprentissage auto-supervisé dans le cadre tabulaire. Plus précisément, notre architecture s’inspire des travaux autour de l’approche SimCLR en modificant l’architecture mean-teacher du domaine du semi-supervisé. Nous avons montré via la win matrix la supériorité de notre approche par rapport à l’état de l’art. Enfin, nous avons proposé d’appliquer les connaissances acquises au cours de ce travail de thèse dans un cadre industriel, celui de Brigad. Nous avons atténué le problème de l’attrition à l’aide des prédictions issues de l’approche de forêt aléatoire que nous avons optimisés via un grid search et l’optimisation des seuils. Nous avons également proposé une interprétation des résultats avec les méthodes SHAP (SHapley Additive exPlanations)<br>The problem of churn prediction has been traditionally a field of study for marketing. However, in the wake of the technological advancements, more and more data can be collected to analyze the customers behaviors. This manuscript has been built in this frame, with a particular focus on machine learning. Thus, we first looked at the supervised learning problem. We have demonstrated that logistic regression, random forest and XGBoost taken as an ensemble offer the best results in terms of Area Under the Curve (AUC) among a wide range of traditional machine learning approaches. We also have showcased that the re-sampling approaches are solely efficient in a local setting and not a global one. Subsequently, we aimed at fine-tuning our prediction by relying on customer segmentation. Indeed,some customers can leave a service because of a cost that they deem to high, and other customers due to a problem with the customer’s service. Our approach was enriched with a novel deep neural network architecture, which operates with both the auto-encoders and the k-means approach. Going further, we focused on self-supervised learning in the tabular domain. More precisely, the proposed architecture was inspired by the work on the SimCLR approach, where we altered the architecture with the Mean-Teacher model from semi-supervised learning. We showcased through the win matrix the superiority of our approach with respect to the state of the art. Ultimately, we have proposed to apply what we have built in this manuscript in an industrial setting, the one of Brigad. We have alleviated the company churn problem with a random forest that we optimized through grid-search and threshold optimization. We also proposed to interpret the results with SHAP (SHapley Additive exPlanations)
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Khan, Umair. "Self-supervised deep learning approaches to speaker recognition." Doctoral thesis, Universitat Politècnica de Catalunya, 2021. http://hdl.handle.net/10803/671496.
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In speaker recognition, i-vectors have been the state-of-the-art unsupervised technique over the last few years, whereas x-vectors is becoming the state-of-the-art supervised technique, these days. Recent advances in Deep Learning (DL) approaches to speaker recognition have improved the performance but are constrained to the need of labels for the background data. In practice, labeled background data is not easily accessible, especially when large training data is required. In i-vector based speaker recognition, cosine and Probabilistic Linear Discriminant Analysis (PLDA) are the two basic scoring techniques. Cosine scoring is unsupervised whereas PLDA parameters are typically trained using speaker-labeled background data. This makes a big performance gap between these two scoring techniques. The question is: how to fill this performance gap without using speaker labels for the background data? In this thesis, the above mentioned problem has been addressed using DL approaches without using and/or limiting the use of labeled background data. Three DL based proposals have been made.
In the first proposal, a Restricted Boltzmann Machine (RBM) vector representation of speech is proposed for the tasks of speaker clustering and tracking in TV broadcast shows. This representation is referred to as RBM vector. The experiments on AGORA database show that in speaker clustering the RBM vectors gain a relative improvement of 12% in terms of Equal Impurity (EI). For speaker tracking task RBM vectors are used only in the speaker identification part, where the relative improvement in terms of Equal Error Rate (EER) is 11% and 7% using cosine and PLDA scoring, respectively.
In the second proposal, DL approaches are proposed in order to increase the discriminative power of i-vectors in speaker verification. We have proposed the use of autoencoder in several ways. Firstly, an autoencoder will be used as a pre-training for a Deep Neural Network (DNN) using a large amount of unlabeled background data. Then, a DNN classifier will be trained using relatively small labeled data. Secondly, an autoencoder will be trained to transform i-vectors into a new representation to increase their discriminative power. The training will be carried out based on the nearest neighbor i-vectors which will be chosen in an unsupervised manner. The evaluation was performed on VoxCeleb-1 database. The results show that using the first system, we gain a relative improvement of 21% in terms of EER, over i-vector/PLDA. Whereas, using the second system, a relative improvement of 42% is gained. If we use the background data in the testing part, a relative improvement of 53% is gained.
In the third proposal, we will train a self-supervised end-to-end speaker verification system. The idea is to utilize impostor samples along with the nearest neighbor samples to make client/impostor pairs in an unsupervised manner. The architecture will be based on a Convolutional Neural Network (CNN) encoder, trained as a siamese network with two branch networks. Another network with three branches will also be trained using triplet loss, in order to extract unsupervised speaker embeddings. The experimental results show that both the end-to-end system and the speaker embeddings, despite being unsupervised, show a comparable performance to the supervised baseline. Moreover, their score combination can further improve the performance.
The proposed approaches for speaker verification have respective pros and cons. The best result was obtained using the nearest neighbor autoencoder with a disadvantage of relying on background i-vectors in the testing. On the contrary, the autoencoder pre-training for DNN is not bound by this factor but is a semi-supervised approach. The third proposal is free from both these constraints and performs pretty reasonably. It is a self-supervised approach and it does not require the background i-vectors in the testing phase.<br>Los avances recientes en Deep Learning (DL) para el reconocimiento del hablante están mejorado el rendimiento de los sistemas tradicionales basados en i-vectors. En el reconocimiento de locutor basado en i-vectors, la distancia coseno y el análisis discriminante lineal probabilístico (PLDA) son las dos técnicas más usadas de puntuación. La primera no es supervisada, pero la segunda necesita datos etiquetados por el hablante, que no son siempre fácilmente accesibles en la práctica. Esto crea una gran brecha de rendimiento entre estas dos técnicas de puntuación. La pregunta es: ¿cómo llenar esta brecha de rendimiento sin usar etiquetas del hablante en los datos de background? En esta tesis, el problema anterior se ha abordado utilizando técnicas de DL sin utilizar y/o limitar el uso de datos etiquetados. Se han realizado tres propuestas basadas en DL. En la primera, se propone una representación vectorial de voz basada en la máquina de Boltzmann restringida (RBM) para las tareas de agrupación de hablantes y seguimiento de hablantes en programas de televisión. Los experimentos en la base de datos AGORA, muestran que en agrupación de hablantes los vectores RBM suponen una mejora relativa del 12%. Y, por otro lado, en seguimiento del hablante, los vectores RBM,utilizados solo en la etapa de identificación del hablante, muestran una mejora relativa del 11% (coseno) y 7% (PLDA). En la segunda, se utiliza DL para aumentar el poder discriminativo de los i-vectors en la verificación del hablante. Se ha propuesto el uso del autocodificador de varias formas. En primer lugar, se utiliza un autocodificador como preentrenamiento de una red neuronal profunda (DNN) utilizando una gran cantidad de datos de background sin etiquetar, para posteriormente entrenar un clasificador DNN utilizando un conjunto reducido de datos etiquetados. En segundo lugar, se entrena un autocodificador para transformar i-vectors en una nueva representación para aumentar el poder discriminativo de los i-vectors. El entrenamiento se lleva a cabo en base a los i-vectors vecinos más cercanos, que se eligen de forma no supervisada. La evaluación se ha realizado con la base de datos VoxCeleb-1. Los resultados muestran que usando el primer sistema obtenemos una mejora relativa del 21% sobre i-vectors, mientras que usando el segundo sistema, se obtiene una mejora relativa del 42%. Además, si utilizamos los datos de background en la etapa de prueba, se obtiene una mejora relativa del 53%. En la tercera, entrenamos un sistema auto-supervisado de verificación de locutor de principio a fin. Utilizamos impostores junto con los vecinos más cercanos para formar pares cliente/impostor sin supervisión. La arquitectura se basa en un codificador de red neuronal convolucional (CNN) que se entrena como una red siamesa con dos ramas. Además, se entrena otra red con tres ramas utilizando la función de pérdida triplete para extraer embeddings de locutores. Los resultados muestran que tanto el sistema de principio a fin como los embeddings de locutores, a pesar de no estar supervisados, tienen un rendimiento comparable a una referencia supervisada. Cada uno de los enfoques propuestos tienen sus pros y sus contras. El mejor resultado se obtuvo utilizando el autocodificador con el vecino más cercano, con la desventaja de que necesita los i-vectors de background en el test. El uso del preentrenamiento del autocodificador para DNN no tiene este problema, pero es un enfoque semi-supervisado, es decir, requiere etiquetas de hablantes solo de una parte pequeña de los datos de background. La tercera propuesta no tienes estas dos limitaciones y funciona de manera razonable. Es un en
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Zhang, Kun. "Supervised and Self-Supervised Learning for Video Object Segmentation in the Compressed Domain." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/29361.
Full textAbstract:
Video object segmentation has attracted remarkable attention since it is more and more critical in real video understanding scenarios. Raw videos have very high redundancies. Therefore, using a heavy backbone network to extract features from all individual frames may be a waste of time. Also, the motion vectors and residuals in compressed videos provide motion information that can be utilized directly. Therefore, this thesis will discuss semi-supervised video object segmentation methods working directly on compressed videos.
First, we discuss a supervised learning method for semi-supervised video object segmentation on compressed videos. To reduce the running time of the model, we design to only use a heavy backbone network for several keyframes. We then employ a much more lightweight network to extract the features for other frames. This operation saves both training and inference time and eliminates redundant information. To our best knowledge, the proposed approach is the fastest video object segmentation model up to now.
Second, we explore a self-supervised learning approach for semi-supervised video object segmentation in the compressed domain. Deep neural networks usually need a massive amount of labeled data to train. However, we can obtain countless image data and video data at almost zero cost. This thesis presents a deep learning-based motion-aware matching approach for semi-supervised video object segmentation using self-supervised learning. A compelling new reconstruction loss is also designed, which is computed between motion information to improve the model's effectiveness.
Experimental results on two public video object segmentation datasets show that our proposed models for the two tasks are efficient and effective, indicating that video object segmentation in the compressed domain is a potential research direction.
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Liu, Dongnan. "Supervised and Unsupervised Deep Learning-based Biomedical Image Segmentation." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/24744.
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Biomedical image analysis plays a crucial role in the development of healthcare, with a wide scope of applications including the disease diagnosis, clinical treatment, and future prognosis. Among various biomedical image analysis techniques, segmentation is an essential step, which aims at assigning each pixel with labels of interest on the category and instance. At the early stage, the segmentation results were obtained via manual annotation, which is time-consuming and error-prone. Over the past few decades, hand-craft feature based methods have been proposed to segment the biomedical images automatically. However, these methods heavily rely on prior knowledge, which limits their generalization ability on various biomedical images. With the recent advance of the deep learning technique, convolutional neural network (CNN) based methods have achieved state-of-the-art performance on various nature and biomedical image segmentation tasks. The great success of the CNN based segmentation methods results from the ability to learn contextual and local information from the high dimensional feature space. However, the biomedical image segmentation tasks are particularly challenging, due to the complicated background components, the high variability of object appearances, numerous overlapping objects, and ambiguous object boundaries. To this end, it is necessary to establish automated deep learning-based segmentation paradigms, which are capable of processing the complicated semantic and morphological relationships in various biomedical images.
In this thesis, we propose novel deep learning-based methods for fully supervised and unsupervised biomedical image segmentation tasks. For the first part of the thesis, we introduce fully supervised deep learning-based segmentation methods on various biomedical image analysis scenarios. First, we design a panoptic structure paradigm for nuclei instance segmentation in the histopathology images, and cell instance segmentation in the fluorescence microscopy images. Traditional proposal-based and proposal-free instance segmentation methods are only capable to leverage either global contextual or local instance information. However, our panoptic paradigm integrates both of them and therefore achieves better performance. Second, we propose a multi-level feature fusion architecture for semantic neuron membrane segmentation in the electron microscopy (EM) images. Third, we propose a 3D anisotropic paradigm for brain tumor segmentation in magnetic resonance images, which enlarges the model receptive field while maintaining the memory efficiency.
Although our fully supervised methods achieve competitive performance on several biomedical image segmentation tasks, they heavily rely on the annotations of the training images. However, labeling pixel-level segmentation ground truth for biomedical images is expensive and labor-intensive. Subsequently, exploring unsupervised segmentation methods without accessing annotations is an important topic for biomedical image analysis. In the second part of the thesis, we focus on the unsupervised biomedical image segmentation methods. First, we proposed a panoptic feature alignment paradigm for unsupervised nuclei instance segmentation in the histopathology images, and mitochondria instance segmentation in EM images. To the best of our knowledge, we are for the first time to design an unsupervised deep learning-based method for various biomedical image instance segmentation tasks. Second, we design a feature disentanglement architecture for unsupervised object recognition. In addition to the unsupervised instance segmentation for the biomedical images, our method also achieves state-of-the-art performance on the unsupervised object detection for natural images, which further demonstrates its effectiveness and high generalization ability.
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Han, Kun. "Supervised Speech Separation And Processing." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1407865723.
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Nasrin, Mst Shamima. "Pathological Image Analysis with Supervised and Unsupervised Deep Learning Approaches." University of Dayton / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1620052562772676.
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Karlsson, Erik, and Gilbert Nordhammar. "Naive semi-supervised deep learning med sammansättning av pseudo-klassificerare." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-17177.
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Ett vanligt problem inom supervised learning är brist på taggad träningsdata. Naive semi-supervised deep learning är en träningsteknik som ämnar att mildra detta problem genom att generera pseudo-taggad data och därefter låta ett neuralt nätverk träna på denna samt en mindre mängd taggad data. Detta arbete undersöker om denna teknik kan förbättras genom användandet av röstning. Flera neurala nätverk tränas genom den framtagna tekniken, naive semi-supervised deep learning eller supervised learning och deras träffsäkerhet utvärderas därefter. Resultaten visade nästan enbart försämringar då röstning användes. Dock verkar inte förutsättningarna för röstning ha varit särskilt goda, vilket gör det svårt att dra en säker slutsats kring effekterna av röstning. Även om röstning inte gav förbättringar har NSSDL visat sig vara mycket effektiv. Det finns flera applikationsområden där tekniken i framtiden skulle kunna användas med goda resultat.
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Örnberg, Oscar. "Semi-Supervised Methods for Classification of Hyperspectral Images with Deep Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-288726.
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Hyperspectral images (HSI) can reveal more patterns than regular images. The dimensionality is high with a wider spectrum for each pixel. Few labeled datasets exists while unlabeled data is abundant. This makes semi-supervised learning well suited for HSI classification. Leveraging new research in deep learning and semi-supervised methods, two models called FixMatch and Mean Teacher was adapted to gauge the effectiveness of consistency regularization methods for semi-supervised learning on HSI classification. Traditional machine learning methods such as SVM, Random Forest and XGBoost was compared in conjunction with two semi-supervised machine learning methods, TSVM and QN-S3VM, as baselines. The semi-supervised deep learning models was tested with two networks, a 3D and 1D CNN. To enable the use of consistency regularization several new data augmentation methods was adapted to the HSI data. Current methods are few and most rely on labeled data, which is not available in this setting. The data augmentation methods presented proved useful and was adapted in a automatic augmentation scheme. The accuracy of the baseline and semi-supervised methods showed that the SVM was best in all cases. Neither semi-supervised method showed consistently better performance than their supervised equivalent.<br>Hyperspektrala bilder (HSI) kan avslöja fler mönster än vanliga bilder. Dimensionaliteten är hög med ett bredare spektrum för varje pixel. Få dataset som är etiketter finns, medan rådata finns i överflöd. Detta gör att semi-vägledd inlärning är väl anpassad för HSI klassificering. Genom att utnyttja nya rön inom djupinlärning och semi-vägledda methods, två modeller kallade FixMatch och Mean Teacher adapterades för att mäta effektiviteten hos konsekvens regularisering metoder inom semi-vägledd inlärning på HSI klassifikation. Traditionella maskininlärnings metoder så som SVM, Random Forest och XGBoost jämfördes i samband med two semi-vägledda maskininlärnings metoder, TSVM och QN-S3VM, som basnivå. De semi-vägledda djupinlärnings metoderna testades med två olika nätverk, en 3D och 1D CNN. För att kunna använda konsekvens regularisering, flera nya data augmenterings metoder adapterades till HSI data. Nuvarande metoder är få och förlitar sig på att datan har etiketter, vilket inte är tillgängligt i detta scenariot. Data augmenterings metoderna som presenterades visade sig vara användbara och adapterades i ett automatiskt augmenteringssystem. Noggrannheten av basnivå och de semi-vägledda metoderna visade att SVM var bäst i alla fall. Ingen av de semi-vägledda metoderna visade konsekvent bättre resultat än deras vägledda motsvarigheter.
Books on the topic "Deep supervised learning"
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Pal, Sujit, Amita Kapoor, Antonio Gulli, and François Chollet. Deep Learning with TensorFlow and Keras: Build and Deploy Supervised, Unsupervised, Deep, and Reinforcement Learning Models. Packt Publishing, Limited, 2022.
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Sawarkar, Kunal, and Dheeraj Arremsetty. Deep Learning with PyTorch Lightning: Build and Train High-Performance Artificial Intelligence and Self-Supervised Models Using Python. Packt Publishing, Limited, 2021.
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Rajakumar, P. S., S. Geetha, and T. V. Ananthan. Fundamentals of Image Processing. Jupiter Publications Consortium, 2023. http://dx.doi.org/10.47715/jpc.b.978-93-91303-80-8.
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"Fundamentals of Image Processing" offers a comprehensive exploration of image processing's pivotal techniques, tools, and applications. Beginning with an overview, the book systematically categorizes and explains the multifaceted steps and methodologies inherent to the digital processing of images. The text progresses from basic concepts like sampling and quantization to advanced techniques such as image restoration and feature extraction. Special emphasis is given to algorithms and models crucial to image enhancement, restoration, segmentation, and application. In the initial segments, the intricacies of digital imaging systems, pixel connectivity, color models, and file formats are dissected. Following this, image enhancement techniques, including spatial and frequency domain methods and histogram processing, are elaborated upon. The book then addresses image restoration, discussing degradation models, noise modeling, and blur, and offers insights into the compelling world of multi-resolution analysis with in-depth discussions on wavelets and image pyramids. Segmentation processes, especially edge operators, boundary detections, and thresholding techniques, are detailed in subsequent chapters. The text culminates by diving deep into the applications of image processing, exploring supervised and unsupervised learning, clustering algorithms, and various classifiers. Throughout the discourse, practical examples, real-world applications, and intuitive diagrams are integrated to facilitate an enriched learning experience. This book stands as an essential guide for both novices aiming to grasp the basics and experts looking to hone their knowledge in image processing. Keywords: Digital Imaging Systems, Image Enhancement, Image Restoration, Multi-resolution Analysis, Wavelets, Image Segmentation, Feature Extraction, SIFT, SURF, Image Classifiers, Supervised Learning, Clustering Algorithms.
Book chapters on the topic "Deep supervised learning"
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Jo, Taeho. "Supervised Learning." In Deep Learning Foundations. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32879-4_2.
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Cerulli, Giovanni. "Deep Learning." In Fundamentals of Supervised Machine Learning. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-41337-7_7.
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Ros, Frederic, and Rabia Riad. "Deep clustering techniques." In Unsupervised and Semi-Supervised Learning. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-48743-9_9.
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Ros, Frederic, and Rabia Riad. "Deep Feature selection." In Unsupervised and Semi-Supervised Learning. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-48743-9_8.
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Wani, M. Arif, Farooq Ahmad Bhat, Saduf Afzal, and Asif Iqbal Khan. "Supervised Deep Learning Architectures." In Studies in Big Data. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6794-6_4.
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Shanthamallu, Uday Shankar, and Andreas Spanias. "Semi-Supervised Learning." In Machine and Deep Learning Algorithms and Applications. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-03758-0_4.
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Ros, Frederic, and Rabia Riad. "Deep clustering techniques: synthesis." In Unsupervised and Semi-Supervised Learning. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-48743-9_13.
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Ros, Frederic, and Rabia Riad. "Chapter 6: Deep learning architectures." In Unsupervised and Semi-Supervised Learning. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-48743-9_6.
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Pratama, Mahardhika, Andri Ashfahani, and Edwin Lughofer. "Unsupervised Continual Learning via Self-adaptive Deep Clustering Approach." In Continual Semi-Supervised Learning. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17587-9_4.
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Zemmari, Akka, and Jenny Benois-Pineau. "Supervised Learning Problem Formulation." In Deep Learning in Mining of Visual Content. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-34376-7_2.
Full textConference papers on the topic "Deep supervised learning"
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Hailat, Zeyad, Artem Komarichev, and Xue-Wen Chen. "Deep Semi-Supervised Learning." In 2018 24th International Conference on Pattern Recognition (ICPR). IEEE, 2018. http://dx.doi.org/10.1109/icpr.2018.8546327.
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Baucum, Michael, Daniel Belotto, Sayre Jeannet, Eric Savage, Prannoy Mupparaju, and Carlos W. Morato. "Semi-supervised Deep Continuous Learning." In the 2017 International Conference. ACM Press, 2017. http://dx.doi.org/10.1145/3094243.3094247.
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Rottmann, Matthias, Karsten Kahl, and Hanno Gottschalk. "Deep Bayesian Active Semi-Supervised Learning." In 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2018. http://dx.doi.org/10.1109/icmla.2018.00031.
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Weston, Jason, Frédéric Ratle, and Ronan Collobert. "Deep learning via semi-supervised embedding." In the 25th international conference. ACM Press, 2008. http://dx.doi.org/10.1145/1390156.1390303.
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Pathapati, Aravind Ganesh, Nakka Chakradhar, P. N. V. S. S. K. Havish, Sai Ashish Somayajula, and Saidhiraj Amuru. "Supervised Deep Learning for MIMO Precoding." In 2020 IEEE 3rd 5G World Forum (5GWF). IEEE, 2020. http://dx.doi.org/10.1109/5gwf49715.2020.9221261.
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Zhang, Junbo, Guangjian Tian, Yadong Mu, and Wei Fan. "Supervised deep learning with auxiliary networks." In KDD '14: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2014. http://dx.doi.org/10.1145/2623330.2623618.
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Alves de Lima, Bruno Vicente, Adriao Duarte Doria Neto, Lucia Emilia Soares Silva, Vinicius Ponte Machado, and Joao Guilherme Cavalcanti Costa. "Semi-supervised Classification Using Deep Learning." In 2019 8th Brazilian Conference on Intelligent Systems (BRACIS). IEEE, 2019. http://dx.doi.org/10.1109/bracis.2019.00158.
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Reader, Andrew J. "Self-supervised and supervised deep learning for PET image reconstruction." In INTERNATIONAL WORKSHOP ON MACHINE LEARNING AND QUANTUM COMPUTING APPLICATIONS IN MEDICINE AND PHYSICS: WMLQ2022. AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0203321.
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Chen, Dong-Dong, Wei Wang, Wei Gao, and Zhi-Hua Zhou. "Tri-net for Semi-Supervised Deep Learning." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/278.
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Deep neural networks have witnessed great successes in various real applications, but it requires a large number of labeled data for training. In this paper, we propose tri-net, a deep neural network which is able to use massive unlabeled data to help learning with limited labeled data. We consider model initialization, diversity augmentation and pseudo-label editing simultaneously. In our work, we utilize output smearing to initialize modules, use fine-tuning on labeled data to augment diversity and eliminate unstable pseudo-labels to alleviate the influence of suspicious pseudo-labeled data. Experiments show that our method achieves the best performance in comparison with state-of-the-art semi-supervised deep learning methods. In particular, it achieves 8.30% error rate on CIFAR-10 by using only 4000 labeled examples.
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Chen, Hung-Yu, and Jen-Tzung Chien. "Deep semi-supervised learning for domain adaptation." In 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2015. http://dx.doi.org/10.1109/mlsp.2015.7324325.
Full textReports on the topic "Deep supervised learning"
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Lin, Youzuo. Physics-guided Machine Learning: from Supervised Deep Networks to Unsupervised Lightweight Models. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/1994110.
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Tran, Anh, Theron Rodgers, and Timothy Wildey. Reification of latent microstructures: On supervised unsupervised and semi-supervised deep learning applications for microstructures in materials informatics. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1673174.
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Mbani, Benson, Timm Schoening, and Jens Greinert. Automated and Integrated Seafloor Classification Workflow (AI-SCW). GEOMAR, 2023. http://dx.doi.org/10.3289/sw_2_2023.
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The Automated and Integrated Seafloor Classification Workflow (AI-SCW) is a semi-automated underwater image processing pipeline that has been customized for use in classifying the seafloor into semantic habitat categories. The current implementation has been tested against a sequence of underwater images collected by the Ocean Floor Observation System (OFOS), in the Clarion-Clipperton Zone of the Pacific Ocean. Despite this, the workflow could also be applied to images acquired by other platforms such as an Autonomous Underwater Vehicle (AUV), or Remotely Operated Vehicle (ROV). The modules in AI-SCW have been implemented using the python programming language, specifically using libraries such as scikit-image for image processing, scikit-learn for machine learning and dimensionality reduction, keras for computer vision with deep learning, and matplotlib for generating visualizations. Therefore, AI-SCW modularized implementation allows users to accomplish a variety of underwater computer vision tasks, which include: detecting laser points from the underwater images for use in scale determination; performing contrast enhancement and color normalization to improve the visual quality of the images; semi-automated generation of annotations to be used downstream during supervised classification; training a convolutional neural network (Inception v3) using the generated annotations to semantically classify each image into one of pre-defined seafloor habitat categories; evaluating sampling strategies for generation of balanced training images to be used for fitting an unsupervised k-means classifier; and visualization of classification results in both feature space view and in map view geospatial co-ordinates. Thus, the workflow is useful for a quick but objective generation of image-based seafloor habitat maps to support monitoring of remote benthic ecosystems.