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Study on training methods and generalization performance of deep learning for image classification

Abstract : Artificial intelligence is experiencing a resurgence in recent years. This is due to the growing ability to collect and store a considerable amount of digitized data. These huge databases allow machine learning algorithms to respond to certain tasks through supervised learning. Among the digitized data, images remain predominant in the modern environment. Huge datasets have been created. moreover, the image classification has allowed the development of previously neglected models, deep neural networks or deep learning. This family of algorithms demonstrates a great facility to learn perfectly datasets, even very large. Their ability to generalize remains largely misunderstood, but the networks of convolutions are today the undisputed state of the art. From a research and application point of view of deep learning, the demands will be more and more demanding, requiring to make an effort to bring the performances of the neuron networks to the maximum of their capacities. This is the purpose of our research, whose contributions are presented in this thesis. We first looked at the issue of training and considered accelerating it through distributed methods. We then studied the architectures in order to improve them without increasing their complexity. Finally, we particularly study the regularization of network training. We studied a regularization criterion based on information theory that we deployed in two different ways.
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Submitted on : Wednesday, June 10, 2020 - 9:55:18 AM
Last modification on : Saturday, July 9, 2022 - 3:19:27 AM


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  • HAL Id : tel-02862841, version 2


Michaël Blot. Study on training methods and generalization performance of deep learning for image classification. Artificial Intelligence [cs.AI]. Sorbonne Université, 2018. English. ⟨NNT : 2018SORUS412⟩. ⟨tel-02862841v2⟩



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