Enhancing CGANs through Integrating Mutual Information

Contents

1. Introduction
2. Problem Description
3. Motivation
4. Methods
   • Architectural Enhancements
   • Mutual Information Maximization
5. Experimental Settings and Results
   • Datasets
   • Training Details
   • Results
   • Figures
6. Conclusion
7. References
8. License

Introduction

This project explores the enhancement of Conditional Generative Adversarial Networks (CGANs) by integrating mutual information to improve the diversity and quality of generated outputs. The project builds on the concept of Information Maximizing GANs (InfoGAN) to ensure that all modes of the data distribution are captured.

Problem Description

Generative Adversarial Networks (GANs) utilize two networks: a generator and a discriminator, in a minimax game to learn to generate new data samples that are indistinguishable from real data. Conditional GANs (CGANs) extend GANs by conditioning the generation process on additional information, improving control and relevance of generated samples. Despite their successes, CGANs face challenges such as mode collapse and instability during training.

InfoGAN is a generative adversarial network that also maximizes the mutual information between a small subset of the latent variables and the observation. Incorporating mutual information can theoretically ensure that all modes of the data distribution are captured, enhancing the diversity and fidelity of the generated samples.

Motivation

The project is motivated by the hypothesis that enhancing CGANs with mutual information can lead to more stable and high-quality generation, particularly in multimodal data settings. Integrating CGANs with InfoGAN could allow the original InfoGAN to generate ordered digits not by categorical code but through conditional labels.

Methods

Architectural Enhancements

Elements from InfoGAN are integrated into the CGAN architecture. This involves adapting the generator of the CGAN to maximize mutual information between a subset of latent codes and the generated outputs. The proposed solution involves solving an information-regularized minimax game:

[ \min_G \max_D V(D, G) = V(D, G) + \lambda I(c; G(z|y, c)) ]

Mutual Information Maximization

Maximizing the mutual information term ( I(c; G(z|y, c)) ) directly is challenging as it requires access to the posterior ( P(c|x) ). Instead, an auxiliary distribution ( Q(c|x) ) is defined to approximate ( P(c|x) ), and a variational lower bound ( L(G|Q) ) of the mutual information is used. The objective function becomes:

[ \min_{G, Q} \max_D V(D, G) = V(D, G) + \lambda L(G|Q) ]

Experimental Settings and Results

Datasets

The MNIST dataset was utilized for digit generation.

Training Details

The experiments were designed based on existing techniques introduced by DC-GAN to stabilize C-InfoGAN training. The training configuration included the use of the Adam optimizer, with specific batch sizes and learning rates adjusted over the course of training.

Results

The experiments aimed to demonstrate that C-InfoGAN can generate more diverse and clearer images and learn disentangled and interpretable representations. Variations in a specific latent code showed that changes affected only one aspect of the image, confirming the hypothesis.

Figures

• Figure 1: Generated MNIST digits, each row conditioned on one label.

• Figure 2: Manipulating Latent Codes on MNIST.

• Figure 3: Manipulating Label and Latent Codes on MNIST.

• Figure 4: Manipulating Categorical Codes and Label on MNIST.

Conclusion

The integration of mutual information led to observable improvements in the diversity and accuracy of generated images across different conditions. Challenges such as computational complexity and the need for fine-tuning the balance between adversarial and information-theoretic components in the loss function were noted. Future work could explore further architecture optimizations and the application of these methods to other types of data beyond images.

References

1. Goodfellow, I. J., et al. (2014). "Generative Adversarial Nets." Neural Information Processing Systems (NIPS).
2. Mirza, M., & Osindero, S. (2014). "Conditional Generative Adversarial Nets." arXiv preprint arXiv:1411.1784.
3. Chen, X., et al. (2016). "InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets." Advances in Neural Information Processing Systems.
4. Belghazi, M. I., et al. (2018). "Mutual Information Neural Estimation." International Conference on Machine Learning (ICML).
5. Radford, A., Metz, L., & Chintala, S. (2015). "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks." arXiv preprint arXiv:1511.06434.
6. Salimans, T., et al. (2016). "Improved techniques for training GANs." Advances in Neural Information Processing Systems.

License

This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.