VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning (1705.07761v3)

Abstract: Deep generative models provide powerful tools for distributions over complicated manifolds, such as those of natural images. But many of these methods, including generative adversarial networks (GANs), can be difficult to train, in part because they are prone to mode collapse, which means that they characterize only a few modes of the true distribution. To address this, we introduce VEEGAN, which features a reconstructor network, reversing the action of the generator by mapping from data to noise. Our training objective retains the original asymptotic consistency guarantee of GANs, and can be interpreted as a novel autoencoder loss over the noise. In sharp contrast to a traditional autoencoder over data points, VEEGAN does not require specifying a loss function over the data, but rather only over the representations, which are standard normal by assumption. On an extensive set of synthetic and real world image datasets, VEEGAN indeed resists mode collapsing to a far greater extent than other recent GAN variants, and produces more realistic samples.

• The paper presents VEEGAN, which integrates a reconstructor network for noise autoencoding to mitigate mode collapse in GANs.
• It employs a modified autoencoder loss on noise vectors to capture a wider range of the underlying data distribution.
• Empirical tests on synthetic, Stacked MNIST, and CIFAR-10 datasets demonstrate improved mode coverage and higher sample quality.

VEEGAN: Reducing Mode Collapse in GANs Using Implicit Variational Learning

The paper "VEEGAN: Reducing Mode Collapse in GANs Using Implicit Variational Learning" presents a novel approach to addressing one of the critical challenges in training Generative Adversarial Networks (GANs): mode collapse. Mode collapse refers to the phenomenon where the generator network learns to produce outputs representing only a few modes of the true data distribution, thus missing various other modes present in the training data.

Key Contributions

The authors introduce VEEGAN, which incorporates a reconstructor network alongside the conventional generator and discriminator networks found in GAN architectures. The primary function of this reconstructor network is to map data points back to the latent noise space, effectively reversing the generator's process. This approach is distinct from traditional autoencoders that operate directly over data points. Instead, VEEGAN autoencodes noise vectors, which allows the model to avoid the difficulties associated with designing a suitable loss function over complex data types, like images.

By utilizing features of variational learning, VEEGAN maintains the asymptotic consistency guarantees typical of GANs. The training objective operates as a modified autoencoder loss applied to noise vectors, thus encouraging the network to capture a broader spectrum of the data distribution's modes and mitigate mode collapse.

Numerical Results and Claims

Empirical evaluations on synthetic data, as well as real-world image datasets such as Stacked MNIST and CIFAR-10, demonstrate that VEEGAN significantly reduces mode collapse compared to traditional and state-of-the-art GAN variants. Key results indicate that VEEGAN captures more modes and produces higher quality samples across the tested datasets.

For instance, in the synthetic 2D ring dataset, VEEGAN captures all eight modes with 52.9% high-quality sample production, outperforming other methods such as ALI, Unrolled GAN, and vanilla GAN. Similarly, on the Stacked MNIST dataset, VEEGAN outperforms alternatives in terms of capturing modes and minimizing skew in the generative distribution, exhibiting a much closer match to the data's actual distribution with a lower KL divergence.

Theoretical and Practical Implications

Theoretically, the approach expands on the traditional GAN framework by incorporating a variational inference perspective. This modification affords insights into the evaluation of implicit probability distributions, further enriching the understanding of adversarial learning's dynamics.

Practically, these enhancements make GANs more robust in applications necessitating a faithful representation of complex, multimodal data distributions. This has implications for various fields reliant on generative models, including image synthesis, data augmentation, and unsupervised representation learning.

Future Directions

Further exploration could focus on scaling VEEGAN to even larger and more complex datasets, as well as adapting the model to different architectural variations within the GAN family. Investigation into the extent of VEEGAN's applicability across domains other than image synthesis may also yield insightful findings. Moreover, a deeper analysis of the reconstructor network’s training dynamics might provide additional avenues for enhancing GAN stability and performance.

In summary, the VEEGAN framework presents a significant, methodical advancement in the pursuit of resolving mode collapse issues in GANs, offering promising alternative pathways for modeling intricate data distributions with higher fidelity.