Relevant bibliographies by topics / Fully- and weakly-Supervised learning

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Fully- and weakly-Supervised learning'

Author: Grafiati
Published: 9 November 2024
Last updated: 31 July 2025

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Journal articles on the topic "Fully- and weakly-Supervised learning"

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Sheng, Taoran, and Manfred Huber. "Reducing Label Dependency in Human Activity Recognition with Wearables: From Supervised Learning to Novel Weakly Self-Supervised Approaches." Sensors 25, no. 13 (2025): 4032. https://doi.org/10.3390/s25134032.

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Human activity recognition (HAR) using wearable sensors has advanced through various machine learning paradigms, each with inherent trade-offs between performance and labeling requirements. While fully supervised techniques achieve high accuracy, they demand extensive labeled datasets that are costly to obtain. Conversely, unsupervised methods eliminate labeling needs but often deliver suboptimal performance. This paper presents a comprehensive investigation across the supervision spectrum for wearable-based HAR, with particular focus on novel approaches that minimize labeling requirements whi
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Cuypers, Suzanna, Maarten Bassier, and Maarten Vergauwen. "Deep Learning on Construction Sites: A Case Study of Sparse Data Learning Techniques for Rebar Segmentation." Sensors 21, no. 16 (2021): 5428. http://dx.doi.org/10.3390/s21165428.

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With recent advancements in deep learning models for image interpretation, it has finally become possible to automate construction site monitoring processes that rely on remote sensing. However, the major drawback of these models is their dependency on large datasets of training images labeled at pixel level, which have to be produced manually by skilled personnel. To alleviate the need for training data, this study evaluates weakly- and semi-supervised semantic segmentation models for construction site imagery to efficiently automate monitoring tasks. As a case study, we compare fully-, weakl
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Wang, Ning, Jiajun Deng, and Mingbo Jia. "Cycle-Consistency Learning for Captioning and Grounding." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 6 (2024): 5535–43. http://dx.doi.org/10.1609/aaai.v38i6.28363.

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We present that visual grounding and image captioning, which perform as two mutually inverse processes, can be bridged together for collaborative training by careful designs. By consolidating this idea, we introduce CyCo, a cyclic-consistent learning framework to ameliorate the independent training pipelines of visual grounding and image captioning. The proposed framework (1) allows the semi-weakly supervised training of visual grounding; (2) improves the performance of fully supervised visual grounding; (3) yields a general captioning model that can describe arbitrary image regions. Extensive
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Wang, Guangyao. "A Study of Object Detection Based on Weakly Supervised Learning." International Journal of Computer Science and Information Technology 2, no. 1 (2024): 476–78. http://dx.doi.org/10.62051/ijcsit.v2n1.50.

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Object detection is one of the important research contents in the field of computer vision. At present, the classical object detection methods can be divided into two categories: fully supervised-based target detection and weakly supervised-based target detection. Since the fully supervised object detection model requires a large number of training data with category labels and target bounding boxes, and such labeled data is difficult to obtain, it is of great significance to explore the weakly supervised object detection method that only needs category label data.
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Xu, Xinyan. "Weakly Supervised Semantic Segmentation with Deep Learning." Applied and Computational Engineering 166, no. 1 (2025): 44–49. https://doi.org/10.54254/2755-2721/2025.tj23839.

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Weakly supervised semantic segmentation aims to achieve segmentation performance comparable to fully supervised methods through low-cost annotation forms such as image level labels or bounding boxes. This article systematically reviews two types of weakly supervised learning methods based on image level labels and bounding box supervision. For image level label supervision, mainstream methods generate initial seed regions through Class Activation Mapping (CAM) and use pixel correlation expansion or iterative optimization strategies (such as erasure and adversarial training) to solve the proble
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Moraes, Daniel, Manuel L. Campagnolo, and Mário Caetano. "A Weakly Supervised and Self-Supervised Learning Approach for Semantic Segmentation of Land Cover in Satellite Images with National Forest Inventory Data." Remote Sensing 17, no. 4 (2025): 711. https://doi.org/10.3390/rs17040711.

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National Forest Inventories (NFIs) provide valuable land cover (LC) information but often lack spatial continuity and an adequate update frequency. Satellite-based remote sensing offers a viable alternative, employing machine learning to extract thematic data. State-of-the-art methods such as convolutional neural networks rely on fully pixel-level annotated images, which are difficult to obtain. Although reference LC datasets have been widely used to derive annotations, NFIs consist of point-based data, providing only sparse annotations. Weakly supervised and self-supervised learning approache
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Adke, Shrinidhi, Changying Li, Khaled M. Rasheed, and Frederick W. Maier. "Supervised and Weakly Supervised Deep Learning for Segmentation and Counting of Cotton Bolls Using Proximal Imagery." Sensors 22, no. 10 (2022): 3688. http://dx.doi.org/10.3390/s22103688.

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The total boll count from a plant is one of the most important phenotypic traits for cotton breeding and is also an important factor for growers to estimate the final yield. With the recent advances in deep learning, many supervised learning approaches have been implemented to perform phenotypic trait measurement from images for various crops, but few studies have been conducted to count cotton bolls from field images. Supervised learning models require a vast number of annotated images for training, which has become a bottleneck for machine learning model development. The goal of this study i
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Dorent, Reuben, Roya Khajavi, Tagwa Idris, et al. "LNQ 2023 challenge: Benchmark of weakly-supervised techniques for mediastinal lymph node quantification." Machine Learning for Biomedical Imaging 3, MICCAI 2023 LNQ challenge (2025): 1–15. https://doi.org/10.59275/j.melba.2025-d482.

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Accurate assessment of lymph node size in 3D CT scans is crucial for cancer staging, therapeutic management, and monitoring treatment response. Existing state-of-the-art segmentation frameworks in medical imaging often rely on fully annotated datasets. However, for lymph node segmentation, these datasets are typically small due to the extensive time and expertise required to annotate the numerous lymph nodes in 3D CT scans. Weakly-supervised learning, which leverages incomplete or noisy annotations, has recently gained interest in the medical imaging community as a potential solution. Despite
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Ni, Ansong, Pengcheng Yin, and Graham Neubig. "Merging Weak and Active Supervision for Semantic Parsing." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 8536–43. http://dx.doi.org/10.1609/aaai.v34i05.6375.

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A semantic parser maps natural language commands (NLs) from the users to executable meaning representations (MRs), which are later executed in certain environment to obtain user-desired results. The fully-supervised training of such parser requires NL/MR pairs, annotated by domain experts, which makes them expensive to collect. However, weakly-supervised semantic parsers are learnt only from pairs of NL and expected execution results, leaving the MRs latent. While weak supervision is cheaper to acquire, learning from this input poses difficulties. It demands that parsers search a large space w
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Colin, Aurélien, Ronan Fablet, Pierre Tandeo, et al. "Semantic Segmentation of Metoceanic Processes Using SAR Observations and Deep Learning." Remote Sensing 14, no. 4 (2022): 851. http://dx.doi.org/10.3390/rs14040851.

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Through the Synthetic Aperture Radar (SAR) embarked on the satellites Sentinel-1A and Sentinel-1B of the Copernicus program, a large quantity of observations is routinely acquired over the oceans. A wide range of features from both oceanic (e.g., biological slicks, icebergs, etc.) and meteorologic origin (e.g., rain cells, wind streaks, etc.) are distinguishable on these acquisitions. This paper studies the semantic segmentation of ten metoceanic processes either in the context of a large quantity of image-level groundtruths (i.e., weakly-supervised framework) or of scarce pixel-level groundtr
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Dissertations / Theses on the topic "Fully- and weakly-Supervised learning"

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Ma, Qixiang. "Deep learning based segmentation and detection of aorta structures in CT images involving fully and weakly supervised learning." Electronic Thesis or Diss., Université de Rennes (2023-....), 2024. http://www.theses.fr/2024URENS029.

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La réparation endovasculaire des anévrismes aortiques abdominaux (EVAR) et l’implantation valvulaire aortique transcathéter (TAVI) sont des interventions endovasculaires pour lesquelles l’analyse des images CT préopératoires est une étape préalable au planning et au guidage de navigation. Dans le cas de la procédure EVAR, les travaux se concentrent spécifiquement sur la question difficile de la segmentation de l’aorte dans l’imagerie CT acquise sans produit de contraste (NCCT), non encore résolue. Dans le cas de la procédure TAVI, ils abordent la détection des repères anatomiques permettant de
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Hlynur, Davíð Hlynsson. "Predicting expert moves in the game of Othello using fully convolutional neural networks." Thesis, KTH, Robotik, perception och lärande, RPL, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210914.

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Careful feature engineering is an important factor of artificial intelligence for games. In this thesis I investigate the benefit of delegating the engineering efforts to the model rather than the features, using the board game Othello as a case study. Convolutional neural networks of varying depths are trained to play in a human-like manner by learning to predict actions from tournaments. My main result is that using a raw board state representation, a network can be trained to achieve 57.4% prediction accuracy on a test set, surpassing previous state-of-the-art in this task.  The accuracy is
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Durand, Thibaut. "Weakly supervised learning for visual recognition." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066142/document.

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Cette thèse s'intéresse au problème de la classification d'images, où l'objectif est de prédire si une catégorie sémantique est présente dans l'image, à partir de son contenu visuel. Pour analyser des images de scènes complexes, il est important d'apprendre des représentations localisées. Pour limiter le coût d'annotation pendant l'apprentissage, nous nous sommes intéressé aux modèles d'apprentissage faiblement supervisé. Dans cette thèse, nous proposons des modèles qui simultanément classifient et localisent les objets, en utilisant uniquement des labels globaux pendant l'apprentissage. L'app
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Durand, Thibaut. "Weakly supervised learning for visual recognition." Electronic Thesis or Diss., Paris 6, 2017. http://www.theses.fr/2017PA066142.

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Cette thèse s'intéresse au problème de la classification d'images, où l'objectif est de prédire si une catégorie sémantique est présente dans l'image, à partir de son contenu visuel. Pour analyser des images de scènes complexes, il est important d'apprendre des représentations localisées. Pour limiter le coût d'annotation pendant l'apprentissage, nous nous sommes intéressé aux modèles d'apprentissage faiblement supervisé. Dans cette thèse, nous proposons des modèles qui simultanément classifient et localisent les objets, en utilisant uniquement des labels globaux pendant l'apprentissage. L'app
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Raisi, Elaheh. "Weakly Supervised Machine Learning for Cyberbullying Detection." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/89100.

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The advent of social media has revolutionized human communication, significantly improving individuals' lives. It makes people closer to each other, provides access to enormous real-time information, and eases marketing and business. Despite its uncountable benefits, however, we must consider some of its negative implications such as online harassment and cyberbullying. Cyberbullying is becoming a serious, large-scale problem damaging people's online lives. This phenomenon is creating a need for automated, data-driven techniques for analyzing and detecting such behaviors. In this research, we
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Hanwell, David. "Weakly supervised learning of visual semantic attributes." Thesis, University of Bristol, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.687063.

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There are at present many billions of images on the internet, only a fraction of which are labelled according to their semantic content. To automatically provide labels for the rest, models of visual semantic concepts must be created. Such models are traditionally trained using images which have been manually acquired, segmented, and labelled. In this thesis, we submit that such models can be learned automatically using those few images which have already been labelled, either directly by their creators, or indirectly by their associated text. Such imagery can be acquired easily, cheaply, and
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Kumar, M. Pawan. "Weakly Supervised Learning for Structured Output Prediction." Habilitation à diriger des recherches, École normale supérieure de Cachan - ENS Cachan, 2013. http://tel.archives-ouvertes.fr/tel-00943602.

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We consider the problem of learning the parameters of a structured output prediction model, that is, learning to predict elements of a complex interdependent output space that correspond to a given input. Unlike many of the existing approaches, we focus on the weakly supervised setting, where most (or all) of the training samples have only been partially annotated. Given such a weakly supervised dataset, our goal is to estimate accurate parameters of the model by minimizing the regularized empirical risk, where the risk is measured by a user-specified loss function. This task has previously be
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Nodet, Pierre. "Biquality learning : from weakly supervised learning to distribution shifts." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG030.

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Le domaine de l'apprentissage avec des faiblesses en supervision est appelé apprentissage faiblement supervisé et regroupe une variété de situations où la vérité terrain collectée est imparfaite. Les étiquettes collectées peuvent souffrir de mauvaise qualité, de non-adaptabilité ou de quantité insuffisante. Dans ce mémoire nous proposons une nouvelle taxonomie de l'apprentissage faiblement supervisé sous la forme d'un cube continu appelé le cube de la supervision faible qui englobe toutes les faiblesses en supervision. Pour concevoir des algorithmes capables de gérer toutes supervisions faible
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Ruiz, Ovejero Adrià. "Weakly-supervised learning for automatic facial behaviour analysis." Doctoral thesis, Universitat Pompeu Fabra, 2017. http://hdl.handle.net/10803/457708.

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In this Thesis we focus on Automatic Facial Behavior Analysis, which attempts to develop autonomous systems able to recognize and understand human facial expressions. Given the amount of information expressed by facial gestures, this type of systems has potential applications in multiple domains such as Human Computer Interaction, Marketing or Healthcare. For this reason, the topic has attracted a lot of attention in Computer Vision and Machine Learning communities during the past two decades. Despite the advances in the field, most of facial expression analysis problems can be considered far
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Siva, Parthipan. "Automatic annotation for weakly supervised learning of detectors." Thesis, Queen Mary, University of London, 2012. http://qmro.qmul.ac.uk/xmlui/handle/123456789/3359.

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Object detection in images and action detection in videos are among the most widely studied computer vision problems, with applications in consumer photography, surveillance, and automatic media tagging. Typically, these standard detectors are fully supervised, that is they require a large body of training data where the locations of the objects/actions in images/videos have been manually annotated. With the emergence of digital media, and the rise of high-speed internet, raw images and video are available for little to no cost. However, the manual annotation of object and action locations rem
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Books on the topic "Fully- and weakly-Supervised learning"

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Munro, Paul. Self-supervised learning of concepts by single units and "weakly local" representations. School of Library and Information Science, University of Pittsburgh, 1988.

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Robert, Tibshirani, and Friedman J. H, eds. The elements of statistical learning: Data mining, inference, and prediction : with 200 full-color illustrations. Springer, 2001.

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Munro, Paul. Self-supervised learning of concepts by single units and "weakly local" representations. School of Library and Information Science, University of Pittsburgh, 1988.

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Unsupervised and Weakly-Supervised Learning of Localized Texture Patterns of Lung Diseases on Computed Tomography. [publisher not identified], 2019.

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Book chapters on the topic "Fully- and weakly-Supervised learning"

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Suresh, Sundaram, Narasimhan Sundararajan, and Ramasamy Savitha. "Fully Complex-valued Relaxation Networks." In Supervised Learning with Complex-valued Neural Networks. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-29491-4_4.

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Tran, Manuel, Sophia J. Wagner, Melanie Boxberg, and Tingying Peng. "S5CL: Unifying Fully-Supervised, Self-supervised, and Semi-supervised Learning Through Hierarchical Contrastive Learning." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16434-7_10.

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Suresh, Sundaram, Narasimhan Sundararajan, and Ramasamy Savitha. "Fully Complex-valued Multi Layer Perceptron Networks." In Supervised Learning with Complex-valued Neural Networks. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-29491-4_2.

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Trivedi, Devharsh, Aymen Boudguiga, and Nikos Triandopoulos. "SigML: Supervised Log Anomaly with Fully Homomorphic Encryption." In Cyber Security, Cryptology, and Machine Learning. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-34671-2_26.

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Baur, Christoph, Shadi Albarqouni, and Nassir Navab. "Semi-supervised Deep Learning for Fully Convolutional Networks." In Medical Image Computing and Computer Assisted Intervention − MICCAI 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66179-7_36.

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Suresh, Sundaram, Narasimhan Sundararajan, and Ramasamy Savitha. "A Fully Complex-valued Radial Basis Function Network and Its Learning Algorithm." In Supervised Learning with Complex-valued Neural Networks. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-29491-4_3.

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Moukafih, Youness, Abdelghani Ghanem, Karima Abidi, Nada Sbihi, Mounir Ghogho, and Kamel Smaili. "SimSCL: A Simple Fully-Supervised Contrastive Learning Framework for Text Representation." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97546-3_59.

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Saha, Pramit, Divyanshu Mishra, and J. Alison Noble. "Rethinking Semi-Supervised Federated Learning: How to Co-train Fully-Labeled and Fully-Unlabeled Client Imaging Data." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43895-0_39.

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Torresani, Lorenzo. "Weakly Supervised Learning." In Computer Vision. Springer US, 2014. http://dx.doi.org/10.1007/978-0-387-31439-6_308.

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Mehmood, Usama, Shouvik Roy, Radu Grosu, Scott A. Smolka, Scott D. Stoller, and Ashish Tiwari. "Neural Flocking: MPC-Based Supervised Learning of Flocking Controllers." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45231-5_1.

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AbstractWe show how a symmetric and fully distributed flocking controller can be synthesized using Deep Learning from a centralized flocking controller. Our approach is based on Supervised Learning, with the centralized controller providing the training data, in the form of trajectories of state-action pairs. We use Model Predictive Control (MPC) for the centralized controller, an approach that we have successfully demonstrated on flocking problems. MPC-based flocking controllers are high-performing but also computationally expensive. By learning a symmetric and distributed neural flocking con
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Conference papers on the topic "Fully- and weakly-Supervised learning"

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Gliga, Lavinius-Ioan, Jeroen Zegers, Carlos Tiana Gomez, and Pieter Bovijn. "Self, Semi and Fully Supervised Learning for Autoencoders using Ternary Classification." In 2024 IEEE International Conference on Prognostics and Health Management (ICPHM). IEEE, 2024. http://dx.doi.org/10.1109/icphm61352.2024.10627326.

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Tang, Yi, Yi Gao, Yong-gang Luo, Ju-Cheng Yang, Miao Xu, and Min-Ling Zhang. "Unlearning from Weakly Supervised Learning." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/553.

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Machine unlearning provides users with the right to remove their privacy data from a well-trained model. Existing approaches of machine unlearning mainly focus on exploring data removing within supervised learning (SL) tasks. However, weakly supervised learning (WSL) is more applicable to real-world scenarios since collecting WSL data is less laborious than collecting fully supervised data. In this paper, we first propose a machine unlearning approach for WSL by updating the model parameters. Motivated by the uniform distributions of untrained model predictions, we derive a formulated target t
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Soltanian-Zadeh, Somayyeh, Kazuhiro Kurokawa, Zhuolin Liu, Daniel X. Hammer, Donald T. Miller, and Sina Farsiu. "Fully automatic quantification of individual ganglion cells from AO-OCT volumes via weakly supervised learning." In Ophthalmic Technologies XXX, edited by Fabrice Manns, Per G. Söderberg, and Arthur Ho. SPIE, 2020. http://dx.doi.org/10.1117/12.2543964.

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Liu, Lu, Tianyi Zhou, Guodong Long, Jing Jiang, Lina Yao, and Chengqi Zhang. "Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category Graph." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/418.

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A variety of machine learning applications expect to achieve rapid learning from a limited number of labeled data. However, the success of most current models is the result of heavy training on big data. Meta-learning addresses this problem by extracting common knowledge across different tasks that can be quickly adapted to new tasks. However, they do not fully explore weakly-supervised information, which is usually free or cheap to collect. In this paper, we show that weakly-labeled data can significantly improve the performance of meta-learning on few-shot classification. We propose prototyp
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Wang, Jiapeng, Tianwei Wang, Guozhi Tang, et al. "Tag, Copy or Predict: A Unified Weakly-Supervised Learning Framework for Visual Information Extraction using Sequences." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/150.

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Visual information extraction (VIE) has attracted increasing attention in recent years. The existing methods usually first organized optical character recognition (OCR) results in plain texts and then utilized token-level category annotations as supervision to train a sequence tagging model. However, it expends great annotation costs and may be exposed to label confusion, the OCR errors will also significantly affect the final performance. In this paper, we propose a unified weakly-supervised learning framework called TCPNet (Tag, Copy or Predict Network), which introduces 1) an efficient enco
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Wu, Yuanchen, Xiaoqiang Li, Songmin Dai, Jide Li, Tong Liu, and Shaorong Xie. "Hierarchical Semantic Contrast for Weakly Supervised Semantic Segmentation." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/171.

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Weakly supervised semantic segmentation (WSSS) with image-level annotations has achieved great processes through class activation map (CAM). Since vanilla CAMs are hardly served as guidance to bridge the gap between full and weak supervision, recent studies explore semantic representations to make CAM fit for WSSS and demonstrate encouraging results. However, they generally exploit single-level semantics, which may hamper the model to learn a comprehensive semantic structure. Motivated by the prior that each image has multiple levels of semantics, we propose hierarchical semantic contrast (HSC
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Dai, Zhigang, Bolun Cai, and Junying Chen. "UniMoCo: Unsupervised, Semi-Supervised and Fully-Supervised Visual Representation Learning." In 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2022. http://dx.doi.org/10.1109/smc53654.2022.9945500.

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Wang, Yifeng, and Yi Zhao. "Scale and Direction Guided GAN for Inertial Sensor Signal Enhancement." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/567.

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Inertial sensors, serving as attitude and motion sensing components, are extensively used in various portable devices spanning consumer electronics, sports health, aerospace, etc. However, the severe intrinsic errors of inertial sensors greatly restrict their capability to implement advanced functions, such as motion tracking and semantic recognition. Although generative models hold significant potential for signal enhancement, unsupervised or weakly-supervised generative methods may not achieve ideal generation results due to the absence of guidance from paired data. To address this, we propo
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Wang, Guanchun, Xiangrong Zhang, Zelin Peng, Xu Tang, Huiyu Zhou, and Licheng Jiao. "Absolute Wrong Makes Better: Boosting Weakly Supervised Object Detection via Negative Deterministic Information." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/192.

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Weakly supervised object detection (WSOD) is a challenging task, in which image-level labels (e.g., categories of the instances in the whole image) are used to train an object detector. Many existing methods follow the standard multiple instance learning (MIL) paradigm and have achieved promising performance. However, the lack of deterministic information leads to part domination and missing instances. To address these issues, this paper focuses on identifying and fully exploiting the deterministic information in WSOD. We discover that negative instances (i.e. absolutely wrong instances), igno
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Pagé Fortin, Mathieu, and Brahim Chaib-draa. "Continual Semantic Segmentation Leveraging Image-level Labels and Rehearsal." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/177.

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Despite the remarkable progress of deep learning models for semantic segmentation, the success of these models is strongly limited by the following aspects: 1) large datasets with pixel-level annotations must be available and 2) training must be performed with all classes simultaneously. Indeed, in incremental learning scenarios, where new classes are added to an existing framework, these models are prone to catastrophic forgetting of previous classes. To address these two limitations, we propose a weakly-supervised mechanism for continual semantic segmentation that can leverage cheap image-le
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Reports on the topic "Fully- and weakly-Supervised learning"

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Nguyen, Minh H., Lorenzo Torresani, Fernando de la Torre, and Carsten Rother. Weakly Supervised Discriminative Localization and Classification: A Joint Learning Process. Defense Technical Information Center, 2009. http://dx.doi.org/10.21236/ada507101.

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