Improving the capabilities of Variational Autoencoder Models by exploring their latent space

Diplôme Thèse Improving the capabilities of Variational Autoencoder Models by exploring their latent space eng 2024 2024 author Lavda Frantzeska 817959 director Marchand-Maillet Stéphane 518563 Département d'informatique (DINFO) director Kalousis Alexandros 514211 Département d'informatique (DINFO) 10.13097/archive-ouverte/unige:178589 urn:nbn:ch:unige-1785895 Sc. 5829 <p>A fundamental goal in developing of Machine Learning is to build systems that are able to mimic the capabilities of humans and animals. This drives the field towards the creation of algorithms and architectures capable of mastering a vast array of tasks, from basic pattern recognition to complex decision-making under and drug design uncertain conditions. Recent advancements in deep learning have shown that neural networks can make significant advances by using large amount of data and computing power. The last decade deep generative models, have achieve great achievements in fields like computer vision, natural language processing, that we could not even imagine two decades ago. These achievements highlight the potential of deep learning and underscore the ongoing effort to narrow the gap between human intelligence and machine intelligence. This thesis delves into the advancement of deep generative modeling, particularly focusing on Variational Autoencoders (VAEs), to tackle significant challenges such as out-ofdistribution (OOD) generation, catastrophic forgetting, and the learning of multi-modal probabilistic structures. Inspired by human cognitive abilities to learn from minimal observations and adapt to new environments, our work seeks to learn similar capabilities within machine learning models, thereby narrowing the gap between human intelligence and artificial intelligence. Through three main contributions, we address limitations of current generative modeling approaches and propose solutions to improve their performance. We explore first, the ability of VAEs to achieve OOD conditional generations. Although conditional generation is already a challenging task because the model might ignore these conditions, our research goes further into a more complex task. As humans’ brains are able to understand and produce new combinations of familiar elements, we develop a novel framework that is capable of generating data with desired property values combinations not included in the training data. Our method, leveraging conditional VAEs with a back-translation mechanism, can handle a diverse range of input–attribute pairs that may not be present in the training data, thus enhancing its capability to handle OOD data. Moreover, the back-translation procedure preserves the content of the input data while manipulating their attribute values, enabling style transfer. Then, we examine another challenging task for ML, namely, continual classification learning. In this thesis, we tackle this challenge by introducing a joint generative model approach, combining naturally a generative model with a classifier in the latent space, relying on the joint generative model to replicate the data distribution with the corresponding labels of the previously seen tasks. Finally, we study the limitations of VAEs, focusing on their inability to produce generations from the individual modalities of data originating from mixture distributions, reflecting humans’ ability to understand and process complex, heterogeneous information. To address this, we propose a 2-level hierarchical latent variable model, which introduces both continuous and categorical latent variables, thereby offering a richer representation of data. By integrating a more flexible variational posterior and an informative conditional prior, mirroring the same structure, our method substantially improves the model’s capacity for capturing and generating the complex probabilistic structures.</p> Dewey 025.063 32 Département d'informatique 18 false 675 Viper group 644c2cbf-378f-4b97-b06c-2285bb43240e Thesis Public phdThesisLavda.pdf 12626416 application/pdf CC BY aa7a5b1ffc539b2a3862c200410cf3470c247448c9ccee6afea8f3b1fe7d1d59 b5430d93-dbcb-4c49-b2c3-3228db6e0e82 Imprimatur Public imprimaturLavda.pdf 386375 application/pdf 6a47c7cf1a8df5b3c43e99654e9579b2cba825fc7dfb8dd37417ff94011ede5f openAccess Faculté des sciences / Département d'informatique

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Improving the capabilities of Variational Autoencoder Models by exploring their latent space				2024	225	217

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