Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive...

Area of Science:

• Artificial Intelligence
• Machine Learning
• Deep Learning

Background:

• Deep generative models utilize deep neural networks to learn data distributions.
• Research in this area is diverse, with various approaches offering different trade-offs.

Purpose of the Study:

• To provide a comprehensive overview of deep generative models.
• To compare and contrast different modeling techniques and their interrelationships.
• To review state-of-the-art advances and implementations.

Main Methods:

• Review of energy-based models, variational autoencoders, generative adversarial networks, autoregressive models, and normalizing flows.
• Analysis of hybrid approaches.
• Comparison of techniques based on runtime, diversity, and architectural constraints.

Main Results:

• Detailed comparison of various deep generative model architectures.
• Explanation of the premises and interrelations between different methods.
• Overview of current advancements and practical implementations.

Conclusions:

• Deep generative models offer diverse approaches to data distribution modeling.
• Understanding the trade-offs is crucial for selecting appropriate techniques.
• The field is rapidly advancing with numerous hybrid and state-of-the-art implementations.