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Dissertations / Theses on the topic 'Generative adversarial model'

Author: Grafiati

Published: 22 February 2023

Last updated: 31 July 2025

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1

Cabezas, Rodríguez Juan Pablo. "Generative adversarial network based model for multi-domain fault diagnosis." Tesis, Universidad de Chile, 2019. http://repositorio.uchile.cl/handle/2250/170996.

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Memoria para optar al título de Ingeniero Civil Mecánico<br>Con el uso de las redes neuronal profundas ganando terreno en el área de PHM, los sensores disminuyendo progresivamente su precio y mejores algoritmos, la falta de datos se ha vuelto un problema principal para los modelos enfocados en datos. Los datos etiquetados y aplicables a escenarios específicos son, en el mejor de los casos, escasos. El objetivo de este trabajo es desarrollar un método para diagnosticas el estado de un rodamiento en situaciones con datos limitados. Hoy en día la mayoría de las técnicas se enfocan en mejora

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Oskarsson, Joel. "Probabilistic Regression using Conditional Generative Adversarial Networks." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166637.

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Regression is a central problem in statistics and machine learning with applications everywhere in science and technology. In probabilistic regression the relationship between a set of features and a real-valued target variable is modelled as a conditional probability distribution. There are cases where this distribution is very complex and not properly captured by simple approximations, such as assuming a normal distribution. This thesis investigates how conditional Generative Adversarial Networks (GANs) can be used to properly capture more complex conditional distributions. GANs have seen gr

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Lenninger, Movitz. "Generative adversarial networks as integrated forward and inverse model for motor control." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-220535.

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Internal models are believed to be crucial components in human motor control. It has been suggested that the central nervous system (CNS) uses forward and inverse models as internal representations of the motor systems. However, it is still unclear how the CNS implements the high-dimensional control of our movements. In this project, generative adversarial networks (GAN) are studied as a generative model of movement data. It is shown that, for a relatively small number of effectors, it is possible to train a GAN which produces new movement samples that are plausible given a simulator environme

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Wu, Xinheng. "A Deep Unsupervised Anomaly Detection Model for Automated Tumor Segmentation." Thesis, The University of Sydney, 2020. https://hdl.handle.net/2123/22502.

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Many researches have been investigated to provide the computer aided diagnosis (CAD) automated tumor segmentation in various medical images, e.g., magnetic resonance (MR), computed tomography (CT) and positron-emission tomography (PET). The recent advances in automated tumor segmentation have been achieved by supervised deep learning (DL) methods trained on large labelled data to cover tumor variations. However, there is a scarcity in such training data due to the cost of labeling process. Thus, with insufficient training data, supervised DL methods have difficulty in generating effective feat

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Nilsson, Mårten. "Augmenting High-Dimensional Data with Deep Generative Models." Thesis, KTH, Robotik, perception och lärande, RPL, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233969.

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Data augmentation is a technique that can be performed in various ways to improve the training of discriminative models. The recent developments in deep generative models offer new ways of augmenting existing data sets. In this thesis, a framework for augmenting annotated data sets with deep generative models is proposed together with a method for quantitatively evaluating the quality of the generated data sets. Using this framework, two data sets for pupil localization was generated with different generative models, including both well-established models and a novel model proposed for this pu

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Yuan, Mengfei. "Machine Learning-Based Reduced-Order Modeling and Uncertainty Quantification for "Structure-Property" Relations for ICME Applications." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1555580083945861.

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Ljung, Mikael. "Synthetic Data Generation for the Financial Industry Using Generative Adversarial Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301307.

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Following the introduction of new laws and regulations to ensure data protection in GDPR and PIPEDA, interests in technologies to protect data privacy have increased. A promising research trajectory in this area is found in Generative Adversarial Networks (GAN), an architecture trained to produce data that reflects the statistical properties of its underlying dataset without compromising the integrity of the data subjects. Despite the technology’s young age, prior research has made significant progress in the generation process of so-called synthetic data, and the current models can generate i

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Tang, Hao. "Learning to Generate Things and Stuff: Guided Generative Adversarial Networks for Generating Human Faces, Hands, Bodies, and Natural Scenes." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/306790.

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In this thesis, we mainly focus on image generation. However, one can still observe unsatisfying results produced by existing state-of-the-art methods. To address this limitation and further improve the quality of generated images, we propose a few novel models. The image generation task can be roughly divided into three subtasks, i.e., person image generation, scene image generation, and cross-modal translation. Person image generation can be further divided into three subtasks, namely, hand gesture generation, facial expression generation, and person pose generation. Meanwhile, scene image

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Tang, Hao. "Learning to Generate Things and Stuff: Guided Generative Adversarial Networks for Generating Human Faces, Hands, Bodies, and Natural Scenes." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/306790.

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In this thesis, we mainly focus on image generation. However, one can still observe unsatisfying results produced by existing state-of-the-art methods. To address this limitation and further improve the quality of generated images, we propose a few novel models. The image generation task can be roughly divided into three subtasks, i.e., person image generation, scene image generation, and cross-modal translation. Person image generation can be further divided into three subtasks, namely, hand gesture generation, facial expression generation, and person pose generation. Meanwhile, scene image

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Lindqvist, Niklas. "Automatic Question Paraphrasing in Swedish with Deep Generative Models." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-294320.

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Paraphrase generation refers to the task of automatically generating a paraphrase given an input sentence or text. Paraphrase generation is a fundamental yet challenging natural language processing (NLP) task and is utilized in a variety of applications such as question answering, information retrieval, conversational systems etc. In this study, we address the problem of paraphrase generation of questions in Swedish by evaluating two different deep generative models that have shown promising results on paraphrase generation of questions in English. The first model is a Conditional Variational

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Jonsson, Jacob. "Cooperative versus Adversarial Learning: Generating Political Text." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-241440.

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This thesis aims to evaluate the current state of the art for unconditional text generation and compare established models with novel approaches in the task of generating texts, after being trained on texts written by political parties from the Swedish Riksdag. First, the progression of language modeling from n-gram models and statistical models to neural network models is presented. This is followed by theoretical arguments for the development of adversarial training methods,where a generator neural network tries to fool a discriminator network, trained to distinguish between real and generat

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Waldow, Walter E. "An Adversarial Framework for Deep 3D Target Template Generation." Wright State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=wright1597334881614898.

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Massaccesi, Luciano. "Machine Learning Software for Automated Satellite Telemetry Monitoring." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20502/.

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During the lifetime of a satellite malfunctions may occur. Unexpected behaviour are monitored using sensors all over the satellite. The telemetry values are then sent to Earth and analysed seeking for anomalies. These anomalies could be detected by humans, but this is considerably expensive. To lower the costs, machine learning techniques can be applied. In this research many diferent machine learning techniques are tested and compared using satellite telemetry data provided by OHB System AG. The fact that the anomalies are collective, together with some data properties, is exploited to impro

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Pagliarini, Silvia. "Modeling the neural network responsible for song learning." Thesis, Bordeaux, 2021. http://www.theses.fr/2021BORD0107.

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Pendant la première période de leur vie, les bébés et les jeunes oiseaux présentent des phases de développement vocal comparables : ils écoutent d'abord leurs parents/tuteurs afin de construire une représentation neurale du stimulus auditif perçu, puis ils commencent à produire des sons qui se rapprochent progressivement du chant de leur tuteur. Cette phase d'apprentissage est appelée la phase sensorimotrice et se caractérise par la présence de babillage. Elle se termine lorsque le chant se cristallise, c'est-à-dire lorsqu'il devient semblable à celui produit par les adultes.Il y a des similit

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Gehlin, Nils, and Martin Antonsson. "Detecting Non-Natural Objects in a Natural Environment using Generative Adversarial Networks with Stereo Data." Thesis, Linköpings universitet, Datorseende, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166619.

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This thesis investigates the use of Generative Adversarial Networks (GANs) for detecting images containing non-natural objects in natural environments and if the introduction of stereo data can improve the performance. The state-of-the-art GAN-based anomaly detection method presented by A. Berget al. in [5] (BergGAN) was the base of this thesis. By modifiying BergGAN to not only accept three channel input, but also four and six channel input, it was possible to investigate the effect of introducing stereo data in the method. The input to the four channel network was an RGB image and its corres

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Chali, Samy. "Robustness Analysis of Classifiers Against Out-of-Distribution and Adversarial Inputs." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPAST012.

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De nombreux problèmes traités par l'IA sont des problèmes de classification de données d'entrées complexes qui doivent être séparées en différentes classes. Les fonctions transformant l'espace complexe des valeurs d'entrées en un espace plus simple, linéairement séparable, se font soit par apprentissage (réseaux convolutionels profonds), soit par projection dans un espace de haute dimension afin d'obtenir une représentation non-linéaire 'riche' des entrées puis un appariement linaire entre l'espace de haute dimension et les unités de sortie, tels qu'utilisés dans les Support Vector Machines (t

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Pakdaman, Hesam. "Updating the generator in PPGN-h with gradients flowing through the encoder." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-224867.

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The Generative Adversarial Network framework has shown success in implicitly modeling data distributions and is able to generate realistic samples. Its architecture is comprised of a generator, which produces fake data that superficially seem to belong to the real data distribution, and a discriminator which is to distinguish fake from genuine samples. The Noiseless Joint Plug & Play model offers an extension to the framework by simultaneously training autoencoders. This model uses a pre-trained encoder as a feature extractor, feeding the generator with global information. Using the Plug &

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Wang, Qi. "Statistical Models for Human Motion Synthesis." Thesis, Ecole centrale de Marseille, 2018. http://www.theses.fr/2018ECDM0005/document.

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Cette thèse porte sur la synthèse de séquences de motion capture avec des modèles statistiques. La synthèse de ce type de séquences est une tâche pertinente pour des domaines d'application divers tels que le divertissement, l'interaction homme-machine, la robotique, etc. Du point de vue de l'apprentissage machine, la conception de modèles de synthèse consiste à apprendre des modèles génératifs, ici pour des données séquentielles. Notre point de départ réside dans deux problèmes principaux rencontrés lors de la synthèse de données de motion capture, assurer le réalisme des positions et des mouv

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Liu, Yahui. "Exploring Multi-Domain and Multi-Modal Representations for Unsupervised Image-to-Image Translation." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/342634.

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Unsupervised image-to-image translation (UNIT) is a challenging task in the image manipulation field, where input images in a visual domain are mapped into another domain with desired visual patterns (also called styles). An ideal direction in this field is to build a model that can map an input image in a domain to multiple target domains and generate diverse outputs in each target domain, which is termed as multi-domain and multi-modal unsupervised image-to-image translation (MMUIT). Recent studies have shown remarkable results in UNIT but they suffer from four main limitations: (1) State-of

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Ghosh, Aishik. "Simulation of the ATLAS electromagnetic calorimeter using generative adversarial networks and likelihood-free inference of the offshell Higgs boson couplings at the LHC." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASP058.

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Depuis la découverte du boson de Higgs en 2012, les expériences du LHC testent les prévisions du modèle standard avec des mesures de haute précision. Les mesures des couplages du boson de Higgs hors résonance permettront d'éliminer certaines dégénérescences qui ne peuvent pas être résolues avec les mesures sur résonance, comme la sonde de la largeur du boson de Higgs, ce qui pourrait donner des indications pour la nouvelle physique. Une partie de cette thèse se concentre sur la mesure des couplages hors résonance du boson de Higgs produit par la fusion du boson vecteur et se décomposant en qua

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Kryściński, Wojciech. "Training Neural Models for Abstractive Text Summarization." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-236973.

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Abstractive text summarization aims to condense long textual documents into a short, human-readable form while preserving the most important information from the source document. A common approach to training summarization models is by using maximum likelihood estimation with the teacher forcing strategy. Despite its popularity, this method has been shown to yield models with suboptimal performance at inference time. This work examines how using alternative, task-specific training signals affects the performance of summarization models. Two novel training signals are proposed and evaluated as

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You-ChengZhu and 朱宥誠. "A Classification Model Based on Generative Adversarial Networks for Breast Cancer." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/u7rz7m.

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CHUANG, FU-CHEN, and 莊馥甄. "Use the Generative Adversarial Network and Attention Model to customize the Neural Style." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/45nr69.

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碩士<br>東吳大學<br>數學系<br>107<br>With the advancement of the information age, the photo app combined with artificial intelligence in the painting method is more and more popular, and the style conversion characteristics are more and more diversified. In the past,there was little use of abstract art. So the purpose of this research is to create novel abstract perception art images, which is to create a new abstract style. It belongs to the artist's unique art, but at the same time it blends with the pictures of the real world.First, using the creative adversarial networks model to create novel abstr

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LIU, CHEN-YU, and 劉宸佑. "Model Training Technique of Traffic Object Trajectory Prediction Based on Generative Adversarial Networks." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/u87ypm.

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碩士<br>國立臺北科技大學<br>資訊工程系<br>107<br>In this paper, we propose an improvement to the architecture of predicting object trajectory based on S-GAN, which predicts future trajectories of traffic objects through GAN (Generative Adversarial Network) and LSTM (Long Short-Term Memory). In addition to the two techniques mentioned above, the algorithm includes the Encoder-Decoder and the concept of adding scene-specific features. Also, we improve the feature processing flow in Discriminator and Generator, making the model attempt to understand more information from scenes, predict a reasonable, human-like

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YANG, HAO-XIANG, and 楊皓翔. "Surface Defect Detection of Scarce Samples Based on Deep Learning Model and Generative Adversarial Network." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/evzn27.

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碩士<br>國立臺北科技大學<br>自動化科技研究所<br>107<br>In traditional automated optical inspection (AOI), the surface defect detection of different targets usually requires the specified detection algorithms and procedures from the field expertise. In order to solve this problem, this thesis used the deep learning model to train the surface defect and further used the data augmentation and generated adversarial network (GAN) to add more abundant training dataset. The sparse defect samples are always happened in surface defect detection. And then, the data augmentation through simple techniques, such as cropping

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RUSSO, PAOLO. "Broadening deep learning horizons: models for RGB and depth images adaptation." Doctoral thesis, 2020. http://hdl.handle.net/11573/1365047.

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Deep Learning has revolutionized the whole field of Computer Vision. Very deep models with an huge number of parameters have been successfully applied on big image datasets for difficult tasks like object classification, person re-identification, semantic segmentation. Two-fold results have been obtained: astonishing performance, with accuracy often comparable or better than a human counterpart on one hand, and on the other the development of robust, complex and powerful visual features which exhibit the ability to generalize to new visual tasks. Still, the success of Deep Learning metho

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(11211114), Qingyi Gao. "ADVERSARIAL LEARNING ON ROBUSTNESS AND GENERATIVE MODELS." Thesis, 2021.

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<div>In this dissertation, we study two important problems in the area of modern deep learning: adversarial robustness and adversarial generative model. In the first part, we study the generalization performance of deep neural networks (DNNs) in adversarial learning. Recent studies have shown that many machine learning models are vulnerable to adversarial attacks, but much remains unknown concerning its generalization error in this scenario. We focus on the $\ell_\infty$ adversarial attacks produced under the fast gradient sign method (FGSM). We establish a tight bound for the adversarial Rade

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Bulusu, Mani Madhoolika. "Interpolation of Digital Elevation Models using Generative Adversarial Networks." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/6058.

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A digital elevation model (DEM) is a three-dimensional representation of elevation data of a terrain such as a terrestrial terrain acquired by a reconnaissance aircraft or a lunar terrain acquired using a Chandrayaan rover. Terrestrial DEMs are used in hydrological modeling, geomorphology, and glaciology. Lunar DEMs can be used to locate natural resources and to identify prospective landing sites for exploratory missions. Hence, high quality, reliable DEMs are of great significance. DEMs are generally captured using LiDAR (Light Detection and Ranging), stereophotogrammetry, and time-of-flight

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Čermák, Vojtěch. "Tvorba nepřátelských vzorů hlubokými generativními modely." Master's thesis, 2021. http://www.nusl.cz/ntk/nusl-448078.

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In the thesis, we explore the prospects of creating adversarial examples using various generative models. We design two algorithms to create unrestricted adversarial examples by perturbing the vectors of latent representation and exploiting the target classifier's decision boundary properties. The first algorithm uses linear interpolation combined with bisection to extract candidate samples near the decision boundary of the targeted classifier. The second algorithm applies the idea behind the FGSM algorithm on vectors of latent representation and uses additional information from gradients to o

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Espindola, Tatiane Sander. "Generative Adversarial Networks applied to Telecom Data - Using GANs to generate synthetic features regarding Wi-Fi signal quality." Master's thesis, 2021. http://hdl.handle.net/10362/119708.

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Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics<br>Wireless networks are, currently, one of the main technologies used to connect people. Considering the constant advancements in the field, the telecom operators must guarantee a high-quality service to keep their customer portfolio. To ensure this high-quality service, it is common the establishment of partnerships with specialized technology companies that deliver software services to monitor the networks and identify faults and respective solutions. Although, a com

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Gidel, Gauthier. "Multi-player games in the era of machine learning." Thesis, 2020. http://hdl.handle.net/1866/24800.

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Parmi tous les jeux de société joués par les humains au cours de l’histoire, le jeu de go était considéré comme l’un des plus difficiles à maîtriser par un programme informatique [Van Den Herik et al., 2002]; Jusqu’à ce que ce ne soit plus le cas [Silveret al., 2016]. Cette percée révolutionnaire [Müller, 2002, Van Den Herik et al., 2002] fût le fruit d’une combinaison sophistiquée de Recherche arborescente Monte-Carlo et de techniques d’apprentissage automatique pour évaluer les positions du jeu, mettant en lumière le grand potentiel de l’apprentissage automatique pour résoudre des jeux. L’ap

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Dokoupil, Patrik. "Vytváření umělých dat pro sestavování policejních fotorekognic." Master's thesis, 2021. http://www.nusl.cz/ntk/nusl-448375.

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Eyewitness identification plays an important role during criminal proceedings and may lead to prosecution and conviction of a suspect. One of the methods of eyewitness identification is a police photo lineup when a collection of photographs is presented to the witness in order to identify the perpetrator of the crime. In the lineup, there is typically at most one photograph (typically exactly one) of the suspect and the remaining photographs are the so-called fillers, i.e. photographs of innocent people. Positive identification of the suspect by the witness may result in charge or conviction o

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"Robust and Generalizable Machine Learning through Generative Models,Adversarial Training, and Physics Priors." Doctoral diss., 2019. http://hdl.handle.net/2286/R.I.54939.

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abstract: Machine learning has demonstrated great potential across a wide range of applications such as computer vision, robotics, speech recognition, drug discovery, material science, and physics simulation. Despite its current success, however, there are still two major challenges for machine learning algorithms: limited robustness and generalizability. The robustness of a neural network is defined as the stability of the network output under small input perturbations. It has been shown that neural networks are very sensitive to input perturbations, and the prediction from convolutional neu

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Hennessy, Andrew James. "Improved hyperspectral classification of vegetation through generative deep learning models." Thesis, 2021. https://hdl.handle.net/2440/133291.

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Early studies into hyperspectral reflectance demonstrated that the spectra of different plants have the potential for taxonomic discrimination and classification, though this came with the caveat that misidentification was a frequent impediment as a result of small sample sizes, inter-class similarity and intra-class variability. The aim of this thesis was to develop methods of improving the ratio between intra and inter-class variability in hyperspectral vegetation spectra, and ultimately increasing classification accuracy, reliability and generalisability. This was addressed in three ways: (

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Pambala, Ayyappa Kumar. "Improved Generative Models for Zero-shot object recognition." Thesis, 2020. https://etd.iisc.ac.in/handle/2005/4708.

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Recognizing objects from their images automatically using computers is an important research area in the computer vision community. In this scenario, the testing images are automatically classified into one of the classes seen during training. But in real-world, new categories arise dynamically, for example, new species of animals or plants are being discovered. To address this issue, zero-shot learning (ZSL) aims at recognizing objects from categories, which has not been encountered during training. If the system has no apriori knowledge whether the input belongs to a seen or unseen class, t

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"Quantifying Information Leakage via Adversarial Loss Functions: Theory and Practice." Doctoral diss., 2020. http://hdl.handle.net/2286/R.I.57087.

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abstract: Modern digital applications have significantly increased the leakage of private and sensitive personal data. While worst-case measures of leakage such as Differential Privacy (DP) provide the strongest guarantees, when utility matters, average-case information-theoretic measures can be more relevant. However, most such information-theoretic measures do not have clear operational meanings. This dissertation addresses this challenge. This work introduces a tunable leakage measure called maximal $\alpha$-leakage which quantifies the maximal gain of an adversary in inferring any functio

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Chih-YuChen and 陳致佑. "Reconstruction of high resolution 3D point cloud models based on Auto-encoder and Generative Adversarial Networks System." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/agsa75.

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碩士<br>國立成功大學<br>工程科學系<br>106<br>In this thesis, a 3D generative system which reconstructs the complete 3D structure of high resolution point clouds from sparse point clouds using an end-to-end autoencoder and generative adversarial networks is proposed. We present the deeplearning and data generation process of the 3D generative system. The key idea of the system is to combine autoencoder and cycle generative adversarial networks framework. The input sparse point clouds are derived from random sampling of a thousand of points in the ground truth point clouds. A paired training approach is use

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Gonçalves, Guilherme Marques. "A comparative study of data augmentation techniques for image classification: generative models vs. classical transformations." Master's thesis, 2020. http://hdl.handle.net/10773/30759.

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Advances in deep convolutional neural networks and efficient parallel processing are showing great promise when applied to image classification, object detection, image restoration and image segmentation. However, deep models require large amounts of annotated training data, which are not always accessible. In this context, data augmentation has appeared as an effective technique by which the original dataset is expanded to cope with imbalanced datasets, avoid overfitting, and increase classification performance. This dissertation aims to compare the effectiveness of data augmentation

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Sarvadevabhatla, Ravi Kiran. "Deep Learning for Hand-drawn Sketches: Analysis, Synthesis and Cognitive Process Models." Thesis, 2018. https://etd.iisc.ac.in/handle/2005/5351.

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Deep Learning-based object category understanding is an important and active area of research in Computer Vision. Most work in this area has predominantly focused on the portion of depiction spectrum consisting of photographic images. However, depictions at the other end of the spectrum, freehand sketches, are a fascinating visual representation and worthy of study in themselves. In this thesis, we present deep-learning approaches for sketch analysis, sketch synthesis and modelling sketch-driven cognitive processes. On the analysis front, we first focus on the problem of recognizing han

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Almahairi, Amjad. "Advances in deep learning with limited supervision and computational resources." Thèse, 2018. http://hdl.handle.net/1866/23434.

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Les réseaux de neurones profonds sont la pierre angulaire des systèmes à la fine pointe de la technologie pour une vaste gamme de tâches, comme la reconnaissance d'objets, la modélisation du langage et la traduction automatique. Mis à part le progrès important établi dans les architectures et les procédures de formation des réseaux de neurones profonds, deux facteurs ont été la clé du succès remarquable de l'apprentissage profond : la disponibilité de grandes quantités de données étiquetées et la puissance de calcul massive. Cette thèse par articles apporte plusieurs contributions à l'avanc

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Dumoulin, Vincent. "Representation Learning for Visual Data." Thèse, 2018. http://hdl.handle.net/1866/21140.

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Bittner, Ksenia. "Building Information Extraction and Refinement from VHR Satellite Imagery using Deep Learning Techniques." Doctoral thesis, 2020. https://repositorium.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-202003262703.

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Building information extraction and reconstruction from satellite images is an essential task for many applications related to 3D city modeling, planning, disaster management, navigation, and decision-making. Building information can be obtained and interpreted from several data, like terrestrial measurements, airplane surveys, and space-borne imagery. However, the latter acquisition method outperforms the others in terms of cost and worldwide coverage: Space-borne platforms can provide imagery of remote places, which are inaccessible to other missions, at any time. Because the manual interpre

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Ganin, Iaroslav. "Natural image processing and synthesis using deep learning." Thèse, 2019. http://hdl.handle.net/1866/23437.

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Nous étudions dans cette thèse comment les réseaux de neurones profonds peuvent être utilisés dans différents domaines de la vision artificielle. La vision artificielle est un domaine interdisciplinaire qui traite de la compréhension d’images et de vidéos numériques. Les problèmes de ce domaine ont traditionnellement été adressés avec des méthodes ad-hoc nécessitant beaucoup de réglages manuels. En effet, ces systèmes de vision artificiels comprenaient jusqu’à récemment une série de modules optimisés indépendamment. Cette approche est très raisonnable dans la mesure où, avec peu de données, el

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