Variational Approaches for Auto-Encoding Generative Adversarial Networks (1706.04987v2)

Abstract: Auto-encoding generative adversarial networks (GANs) combine the standard GAN algorithm, which discriminates between real and model-generated data, with a reconstruction loss given by an auto-encoder. Such models aim to prevent mode collapse in the learned generative model by ensuring that it is grounded in all the available training data. In this paper, we develop a principle upon which auto-encoders can be combined with generative adversarial networks by exploiting the hierarchical structure of the generative model. The underlying principle shows that variational inference can be used a basic tool for learning, but with the in- tractable likelihood replaced by a synthetic likelihood, and the unknown posterior distribution replaced by an implicit distribution; both synthetic likelihoods and implicit posterior distributions can be learned using discriminators. This allows us to develop a natural fusion of variational auto-encoders and generative adversarial networks, combining the best of both these methods. We describe a unified objective for optimization, discuss the constraints needed to guide learning, connect to the wide range of existing work, and use a battery of tests to systematically and quantitatively assess the performance of our method.

• The paper introduces a hybrid AE-GAN model that leverages variational inference with discriminators to effectively mitigate mode collapse.
• It derives a unified objective function that integrates adversarial and reconstruction losses to promote diverse and high-quality sample generation.
• Experiments on datasets like ColorMNIST, CelebA, and CIFAR-10 demonstrate improved performance in sample diversity and quality compared to traditional GAN approaches.

Overview of "Variational Approaches for Auto-Encoding Generative Adversarial Networks"

The paper "Variational Approaches for Auto-Encoding Generative Adversarial Networks" presents a novel approach to improving generative adversarial networks (GANs) by integrating auto-encoding techniques. This integration aims to address the persistent issue of mode collapse in GANs, where the generated data lacks diversity compared to the true data distribution. By leveraging the strengths of variational auto-encoders (VAEs), the authors propose a hybrid model that combines the best features of both methods.

Key Contributions

The paper makes several significant contributions to the field of generative models:

1. Variational Inference with Discriminators: The authors demonstrate how variational inference can be effectively applied to GANs by using discriminators to learn implicit posterior and synthetic likelihood distributions. This approach leverages the density ratio estimation concept within GANs to address the intractability issues in traditional generative models.
2. Unified Objective Function: The paper provides a principled derivation of a unified objective function for auto-encoding GANs (AE-GANs). This function combines both adversarial and reconstruction losses, encouraging the model to learn a diverse representation of the data.
3. Evaluation Metrics and Experimentation: The paper emphasizes the challenges in evaluating generative models and proposes the use of various evaluation metrics to assess model performance rigorously. The experiments conducted on datasets such as ColorMNIST, CelebA, and CIFAR-10 provide a comprehensive comparison between the proposed model and alternative GAN variants.

Numerical Results and Impact

The experiments show that the proposed hybrid model exhibits competitive performance across multiple benchmarks. Notably, the model reduces the phenomenon of mode collapse by ensuring diverse sample generation through its reconstruction loss component. The integration of adversarial training with VAE methods also enhances sample quality, addressing the common VAE issue of producing blurry images.

Theoretical and Practical Implications

The paper bridges a theoretical gap by elucidating the relationship between auto-encoders and GANs through variational inference principles. This understanding facilitates the development of new hybrid models that can overcome the limitations of each method independently. Practically, the resulting model can be applied to tasks requiring both data synthesis and reconstruction, such as image inpainting and representation learning.

Speculation on Future Developments

Given the promising results of this hybrid approach, future research could explore extensions to other types of generative models or enhancements to address other GAN challenges, such as training stability. Additionally, future work could involve applying these concepts to large-scale datasets or more complex tasks in machine learning.

In summary, this paper provides a robust framework for combining the strengths of VAEs and GANs. Its insights into achieving more diverse and realistic generative models are poised to influence future research and applications in generative model development.