Relevant bibliographies by topics / Deep Convolutional Generative Adversarial Networks (DCGAN) / Dissertations / Theses
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Dissertations / Theses on the topic 'Deep Convolutional Generative Adversarial Networks (DCGAN)'

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Published: 5 June 2025
Last updated: 1 August 2025

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1

Adhikari, Aakriti. "Skin Cancer Detection using Generative Adversarial Networkand an Ensemble of deep Convolutional Neural Networks." University of Toledo / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1574383625473665.

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2

Johansson, Philip. "Incremental Learning of Deep Convolutional Neural Networks for Tumour Classification in Pathology Images." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158225.

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Medical doctors understaffing is becoming a compelling problem in many healthcare systems. This problem can be alleviated by utilising Computer-Aided Diagnosis (CAD) systems to substitute doctors in different tasks, for instance, histopa-thological image classification. The recent surge of deep learning has allowed CAD systems to perform this task at a very competitive performance. However, a major challenge with this task is the need to periodically update the models with new data and/or new classes or diseases. These periodical updates will result in catastrophic forgetting, as Convolutional
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3

Albertazzi, Riccardo. "A study on the application of generative adversarial networks to industrial OCR." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.

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High performance and nearly perfect accuracy are the standards required by OCR algorithms for industrial applications. In the last years research on Deep Learning has proven that Convolutional Neural Networks (CNNs) are a very powerful and robust tool for image analysis and classification; when applied to OCR tasks, CNNs are able to perform much better than previously adopted techniques and reach easily 99% accuracy. However, Deep Learning models' effectiveness relies on the quality of the data used to train them; this can become a problem since OCR tools can run for months without interrupti
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Lidberg, Love. "Object Detection using deep learning and synthetic data." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-150555.

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This thesis investigates how synthetic data can be utilized when training convolutional neural networks to detect flags with threatening symbols. The synthetic data used in this thesis consisted of rendered 3D flags with different textures and flags cut out from real images. The synthetic data showed that it can achieve an accuracy above 80% compared to 88% accuracy achieved by a data set containing only real images. The highest accuracy scored was achieved by combining real and synthetic data showing that synthetic data can be used as a complement to real data. Some attempts to improve the ac
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5

Oquab, Maxime. "Convolutional neural networks : towards less supervision for visual recognition." Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEE061.

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Les réseaux de neurones à convolution sont des algorithmes d’apprentissage flexibles qui tirent efficacement parti des importantes masses de données qui leur sont fournies pour l’entraînement. Malgré leur utilisation dans des applications industrielles dès les années 90, ces algorithmes n’ont pas été utilisés pour la reconnaissance d’image à cause de leurs faibles performances avec les images naturelles. C’est finalement grâce a l’apparition d’importantes quantités de données et de puissance de calcul que ces algorithmes ont pu révéler leur réel potentiel lors de la compétition ImageNet, menan
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6

Yedroudj, Mehdi. "Steganalysis and steganography by deep learning." Thesis, Montpellier, 2019. http://www.theses.fr/2019MONTS095.

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La stéganographie d'image est l'art de la communication secrète dans le but d'échanger un message de manière furtive. La stéganalyse d'image a elle pour objectif de détecter la présence d'un message caché en recherchant les artefacts présent dans l'image. Pendant une dizaine d'années, l'approche classique en stéganalyse a été d'utiliser un ensemble classifieur alimenté par des caractéristiques extraites "à la main". Au cours des dernières années, plusieurs études ont montré que les réseaux de neurones convolutionnels peuvent atteindre des performances supérieures à celles des approches convent
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Staccone, Francesco. "Deep Learning for Sea-Ice Classification on Synthetic Aperture Radar (SAR) Images in Earth Observation : Classification Using Semi-Supervised Generative Adversarial Networks on Partially Labeled Data." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-277920.

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Earth Observation is the gathering of information about planet Earth’s system via Remote Sensing technologies for monitoring land cover types and their changes. Through the years, image classification techniques have been widely studied and employed to extract useful information from Earth Observation data such as satellite imagery. One of the most attractive use cases is the monitoring of polar regions, that recently observed some dramatic changes due to global warming. Indeed drifting ice caps and icebergs represent threats to ship activities and navigation in polar areas, and the risk of co
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Tirumaladasu, Sai Subhakar, and Shirdi Manjunath Adigarla. "Autonomous Driving: Traffic Sign Classification." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-17783.

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Autonomous Driving and Advance Driver Assistance Systems (ADAS) are revolutionizing the way we drive and the future of mobility. Among ADAS, Traffic Sign Classification is an important technique which assists the driver to easily interpret traffic signs on the road. In this thesis, we used the powerful combination of Image Processing and Deep Learning to pre-process and classify the traffic signs. Recent studies in Deep Learning show us how good a Convolutional Neural Network (CNN) is for image classification and there are several state-of-the-art models with classification accuracies over 99
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9

Chowdhury, Muhammad Iqbal Hasan. "Question-answering on image/video content." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/205096/1/Muhammad%20Iqbal%20Hasan_Chowdhury_Thesis.pdf.

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This thesis explores a computer's ability to understand multimodal data where the correspondence between image/video content and natural language text are utilised to answer open-ended natural language questions through question-answering tasks. Static image data consisting of both indoor and outdoor scenes, where complex textual questions are arbitrarily posed to a machine to generate correct answers, was examined. Dynamic videos consisting of both single-camera and multi-camera settings for the exploration of more challenging and unconstrained question-answering tasks were also considered. I
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10

MAGGIOLO, LUCA. "Deep Learning and Advanced Statistical Methods for Domain Adaptation and Classification of Remote Sensing Images". Doctoral thesis, Università degli studi di Genova, 2022. http://hdl.handle.net/11567/1070050.

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In the recent years, remote sensing has faced a huge evolution. The constantly growing availability of remote sensing data has opened up new opportunities and laid the foundations for many new challenges. The continuous space missions and new constellations of satellites allow in fact more and more frequent acquisitions, at increasingly higher spatial resolutions, and at an almost total coverage of the globe. The availability of such an huge amount data has highlighted the need for automatic techniques capable of processing the data and exploiting all the available information. Meanwhile, the
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11

Wei, Wen. "Apprentissage automatique des altérations cérébrales causées par la sclérose en plaques en neuro-imagerie multimodale." Thesis, Université Côte d'Azur, 2020. http://www.theses.fr/2020COAZ4021.

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La sclérose en plaques (SEP) est la maladie neurologique évolutive la plus courante chez les jeunes adultes dans le monde et représente donc un problème de santé publique majeur avec environ 90 000 patients en France et plus de 500 000 personnes atteintes de SEP en Europe. Afin d'optimiser les traitements, il est essentiel de pouvoir mesurer et suivre les altérations cérébrales chez les patients atteints de SEP. En fait, la SEP est une maladie aux multiples facettes qui implique différents types d'altérations, telles que les dommages et la réparation de la myéline. Selon cette observation, la
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12

Brito, João Pedro da Cruz. "Deep Adversarial Frameworks for Visually Explainable Periocular Recognition." Master's thesis, 2021. http://hdl.handle.net/10400.6/11850.

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Machine Learning (ML) models have pushed state­of­the­art performance closer to (and even beyond) human level. However, the core of such algorithms is usually latent and hardly understandable. Thus, the field of Explainability focuses on researching and adopting techniques that can explain the reasons that support a model’s predictions. Such explanations of the decision­making process would help to build trust between said model and the human(s) using it. An explainable system also allows for better debugging, during the training phase, and fixing, upon deployment. But why should a developer d
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Mopuri, Konda Reddy. "Deep Visual Representations: A study on Augmentation, Visualization, and Robustness." Thesis, 2018. https://etd.iisc.ac.in/handle/2005/5446.

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Deep neural networks have resulted in unprecedented performances for various learning tasks. Particularly, Convolutional Neural Networks (CNNs) are shown to learn representations that can efficiently discriminate hundreds of visual categories. They learn a hierarchy of representations ranging from low level edge and blob detectors to semantic features such as object categories. These representations can be employed as off-the-shelf visual features in various vision tasks such as image classification, scene retrieval, caption generation, etc.In this thesis, we investigate three important a
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14

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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15

Del, Chiaro Riccardo. "Anthropomorphous Visual Recognition: Learning with Weak Supervision, with Scarce Data, and Incrementally over Transient Tasks." Doctoral thesis, 2021. http://hdl.handle.net/2158/1238101.

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In the last eight years the computer vision field has experienced dramatic improvements thanks to the widespread availability of data and affordable parallel computing hardware like GPUs. These two factors have contributed to making possible the training of very deep neural network models in reasonable times using millions of labeled examples for supervision. Humans do not learn concepts in this way. We do not need a massive number of labeled examples to learn new concepts; instead we rely on a few (or even zero) examples, infer missing information, and generalize. Moreover, we retain previous
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16

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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17

Dutil, Francis. "Prédiction et génération de données structurées à l'aide de réseaux de neurones et de décisions discrètes." Thèse, 2018. http://hdl.handle.net/1866/22124.

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