Deep variational generative models : theory and algorithms
- Publication Type:
- Thesis
- Issue Date:
- 2025
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Deep Variational Generative Models (DVGMs) represent a powerful class of generative models that combine variational inference with deep learning architectures. By leveraging the representational strength of deep neural networks and the probabilistic framework of variational inference, DVGMs have advanced the ability to model complex, high-dimensional data distributions, enabling them to effectively handle images, sequences, time-series, and tabular data, thereby extending their impact across machine learning, computer vision, data analysis, and natural language processing. These models, by uniting the strengths of deep learning with Bayesian principles, provide a flexible approach to understanding intricate data structures and have opened new pathways for efficient representation learning and high-quality generation.
Despite their strengths, DVGMs face notable gaps between probabilistic inference and deep generation, raising several key questions: (1) How can DVGMs balance Bayesian inference with the depth required for generative tasks? (2) How can they manage the trade-off between inference-driven representation and data fitting? (3) How can inference assumptions be leveraged to ensure robust generation? (4) How do probabilistic assumptions be designed to generate cross-modality? (5) How can DVGMs achieve consistent inference within dynamic generation processes? These questions underscore the challenges limiting DVGMs’ potential in practical applications requiring flexible, reliable, and interpretable data generation.
This thesis systematically studies how to effectively address these challenges, providing both experimental and theoretical support. Given the intricate balance required between inference and generation in DVGMs, it is crucial to integrate information-theoretic principles and adaptive mechanisms to enhance DVGM performance across diverse tasks. Specifically, this thesis proposes five novel methods to tackle these issues. The main ideas include employing information-theoretic approaches to train DVGMs, introducing adaptive balancing mechanisms to dynamically adjust inference and generation based on data characteristics, and designing task-specific DVGM structures tailored for various data types. These innovations aim to strengthen representation disentanglement, improve robustness to noise, and increase scalability, enabling DVGMs to handle high-dimensional, noisy, and time-sensitive data effectively. Through these advancements, the thesis establishes a solid foundation for enhancing DVGMs’ applicability in complex, real-world scenarios and provides new directions for future research in generative modeling.
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