MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networks (1903.06048v4)

Abstract: While Generative Adversarial Networks (GANs) have seen huge successes in image synthesis tasks, they are notoriously difficult to adapt to different datasets, in part due to instability during training and sensitivity to hyperparameters. One commonly accepted reason for this instability is that gradients passing from the discriminator to the generator become uninformative when there isn't enough overlap in the supports of the real and fake distributions. In this work, we propose the Multi-Scale Gradient Generative Adversarial Network (MSG-GAN), a simple but effective technique for addressing this by allowing the flow of gradients from the discriminator to the generator at multiple scales. This technique provides a stable approach for high resolution image synthesis, and serves as an alternative to the commonly used progressive growing technique. We show that MSG-GAN converges stably on a variety of image datasets of different sizes, resolutions and domains, as well as different types of loss functions and architectures, all with the same set of fixed hyperparameters. When compared to state-of-the-art GANs, our approach matches or exceeds the performance in most of the cases we tried.

An Overview of Multi-Scale Gradients for GANs: MSG-GAN

The paper "Multi-Scale Gradients for Generative Adversarial Networks," authored by Animesh Karnewar and Oliver Wang, presents a novel approach to enhance the stability and performance of Generative Adversarial Networks (GANs). Specifically, the authors propose the Multi-Scale Gradient GAN (MSG-GAN) framework, which, unlike traditional GAN training methods that primarily focus on a single resolution, involves synthesizing images at multiple scales concurrently. This strategy addresses critical training instability issues associated with GANs, primarily those stemming from uninformative gradients caused by minimal overlap between real and generated data distributions.

Overview

The MSG-GAN leverages the concept of allowing the gradient flow from the discriminator to the generator at multiple resolutions, thereby stabilizing the learning process across varying dataset sizes, resolutions, and domains. Unlike the progressive growing technique utilized in other architectures such as ProGAN, MSG-GAN does not adopt a staged training approach. Instead, it integrates multiscale gradient propagation into a single, seamless process.

Architectural Design

The MSG-GAN architecture introduces several modifications. The generator produces intermediate outputs at each scale, which are fed to the discriminator alongside the final high-resolution output. This characteristic allows the discriminator to pass informative gradients to all layers of the generator, facilitating improved learning outcomes. This is a departure from traditional techniques where only the final output resolution is considered. Notably, this methodology can be applied to architectures like ProGANs and StyleGAN, resulting in MSG-ProGAN and MSG-StyleGAN variants respectively.

Empirical Evaluation

The authors conduct extensive experiments across diverse datasets, such as CIFAR-10, Oxford Flowers, LSUN Churches, CelebA-HQ, FFHQ, and a new Indian Celebs dataset. These experiments illustrate that MSG-GAN achieves superior or comparable Fréchet Inception Distance (FID) scores relative to existing state-of-the-art methods. For example, MSG-GAN outperforms or matches ProGAN and StyleGAN approaches across several datasets while maintaining robustness to different loss functions and hyperparameters.

Theoretical and Practical Implications

MSG-GAN's multiscale gradient approach addresses pivotal issues of training instability and the sensitivity of GANs to hyperparameter choices. By facilitating the flow of gradients at multiple scales, the architecture enhances the capacity of GANs to generate high-resolution images in a more stable and consistent manner. This approach eliminates the need for progressive growing—a technique that complicates the training process with additional hyperparameters such as resolution-specific learning rates and fade-in transitions.

Future Directions

The paper opens several avenues for future research in the field of high-resolution image synthesis. One potential direction is the integration of MSG-GAN with other advanced generative modeling techniques that might benefit from multiscale gradient propagation. Another area of interest is the exploration of adaptive strategies to optimize the combine functions, which could further refine the training dynamics of GANs. Also, investigating the application of MSG-GAN in domains beyond image synthesis, such as video or 3D model generation, could yield compelling results.

Conclusion

MSG-GAN provides a significant step forward in the stabilization and effectiveness of GAN-based image synthesis. Its design, which emphasizes concurrent multiscale image generation and gradient flow, offers a robust, hyperparameter-insensitive framework suitable for a wide range of datasets and image resolutions. This work contributes meaningfully to ongoing efforts in the field of generative modeling to achieve consistently stable and high-quality image synthesis.