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Generative Adversarial Networks

Gilad Cohen, Raja Giryes

arXiv:2203.00667v1cs.CVcs.LG

TL;DR

GANs face inherent training and evaluation challenges, including mode collapse, vanishing gradients, and low-quality images. This chapter explains the adversarial framework, reviews variants addressing these problems, and outlines applications while positioning readers to navigate the literature.

  • Problem

    The original GAN model faces inherent challenges in training and evaluation, including mode collapse, vanishing gradients, and low-quality image generation.

  • Method

    The chapter explains the generator–discriminator mechanism and reviews architecture- and loss-based GAN variants for addressing identified challenges.

  • Results

    The chapter provides an overview intended to help readers understand GANs and navigate their expanding literature.

  • Takeaways & Limitations

    GAN research spans many variants and applications, while normalizing flows and score-based generative models show competitive performance with GANs.

  • Takeaways & Limitations

    The original GAN model’s inherent challenges are improved by follow-up works on one or more problems rather than uniformly resolved.

Abstract

from arXiv · show

Generative Adversarial Networks (GANs) are very popular frameworks for generating high-quality data, and are immensely used in both the academia and industry in many domains. Arguably, their most substantial impact has been in the area of computer vision, where they achieve state-of-the-art image generation. This chapter gives an introduction to GANs, by discussing their principle mechanism and presenting some of their inherent problems during training and evaluation. We focus on these three issues: (1) mode collapse, (2) vanishing gradients, and (3) generation of low-quality images. We then list some architecture-variant and loss-variant GANs that remedy the above challenges. Lastly, we present two utilization examples of GANs for real-world applications: Data augmentation and face images generation.

1 Introduction to GANs

GANs learn a dataset’s distribution to generate new examples through jointly trained generator and discriminator agents. Their adversarial framework has driven successful applications across multiple domains, including computer vision.

  • GANs learn a dataset’s distribution and generate new examples from it.
  • Adversarial training makes generated money increasingly indistinguishable from real money after many iterations.The system consists of counterfeiters creating close-to-real money and police detecting counterfeit bills.
  • The generator creates adversarial samples to fool the discriminator, which distinguishes real data from fake samples.The two agents are trained jointly.
  • Since its introduction in 2014, GAN architecture has been used successfully in many domains, including computer vision.

2 The basic GAN concept

GANs use two jointly trained neural networks: a discriminator distinguishes real from generated images, while a generator transforms random noise into samples intended to match the real-world distribution. Training alternates discriminator and generator updates, and under certain conditions the generated distribution converges to the real distribution.

  • Architecture: GANs comprise a discriminator D that distinguishes real images from generated images and a generator G that crafts fake images to fool D.The generator receives random input z ∼ p_z, producing G(z) ∼ p_g.
  • Objective: The generator learns a generated distribution p_g that estimates the real-world distribution p_r.GANs seek to learn generated samples’ distribution from random inputs.
  • Objective: The discriminator predicts D(x) = 1 for real samples and D(G(z)) = 0 for fake samples, while the generator is optimized to hamper the fake-sample term.This adversarial objective trains the generator to fool the discriminator.
  • Training procedure: Training alternates minibatch updates: discriminator weights θ_D ascend their gradient term, then generator weights θ_G descend theirs.Each iteration updates only one network’s weights using sampled noise and, for the discriminator, real data examples.
  • Convergence: Under certain conditions on D, G, and training, the generated distribution converges to the real distribution.Goodfellow et al. reported this convergence result for the GAN training procedure.

3 GAN Advantages and Problems

GANs offer sharper, configurable, and versatile generation than alternatives such as VAEs, but the original framework faces mode collapse, vanishing gradients, and poor image quality. These challenges arise from adversarial-loss behavior and motivate follow-up work using alternative losses and architectures.

  • Advantages: GANs generate sharper, more natural images than VAEs, while allowing unrestricted latent-variable size and diverse generator architectures.These advantages expand the generator search space and avoid architectural constraints found in some other generative models.
  • Mode collapse: Mode collapse occurs when the generator learns a specific pattern that fools the discriminator instead of covering the dataset’s full distribution.For multimodal data such as MNIST, this can reduce generation to only a small subset of possible modes, such as one digit.
  • Vanishing gradients: Vanishing gradients occur when the discriminator becomes too effective, leaving the generator with extremely low gradients and infeasible training.The JS-divergence gradient vanishes after a generated-distribution mean of 30, especially early when generator weights are randomized.
  • Loss-related instability: Changing the generator objective preserves higher early gradients but promotes mode collapse, so the original loss problems cannot be solved by architecture alone.Alternative loss functions are introduced to address these inherent trade-offs.
  • Image quality and training instability: The original generator losses produce unstable cost behavior and poor-quality images, with loss values showing little correlation with generated image quality.One loss becomes constant after initial steps, while the alternative fluctuates throughout training; modern losses improve resolution and image size.
  • Problems: GANs’ three inherent challenges are mode collapse, vanishing gradients, and image quality.Follow-up works improve performance on one or more of these challenges.

4 Improved GAN architectures

Improved GAN architectures address limitations of the original GAN by conditioning generation, stabilizing training, progressively increasing resolution, using self-attention and scaling, or disentangling image attributes. These approaches improve controllability, image quality, resolution, and training stability across tasks.

  • SGAN: SGAN extends GANs to semi-supervised learning by adding a label-classification task to the discriminator while retaining the original generator architecture.Its discriminator uses sigmoid and softmax heads, with the softmax predicting labels only for images classified as real.
  • CGAN: CGAN conditions both generator and discriminator on image labels, improving real/fake classification and control over generated-image modalities.The architecture can also handle multimodal labeled datasets when combined with a language model.
  • DCGAN: DCGAN uses deconvolutional generators to create high-resolution images and improves training stability through architectural modifications.These include replacing pooling, applying batch normalization, and using Leaky-ReLU activations in the discriminator.
  • PROGAN: PROGAN progressively trains GANs from 4×4 resolution upward, producing credible images and more stable learning for both discriminator and generator.The method incrementally adds trainable layers to increase image resolution.
  • BigGAN: BigGAN combines self-attention with larger models and batches to achieve state-of-the-art ImageNet generation at resolutions up to 512 × 512.It scales network parameters by ×4 and batch size by ×8, while self-attention captures global semantics and long-range dependencies.
  • StyleGAN: StyleGAN starts from a learned constant, modulates features with learned latent variables, and injects noise throughout the generator to separate high- and low-level attributes.It achieves state-of-the-art face-generation quality and greater latent-space disentanglement without modifying the discriminator or loss.

5 Improved GAN objectives

This section surveys GAN objectives and regularizations that address mode collapse, vanishing gradients, training instability, and image quality. It highlights WGAN, SSGAN, SNGAN, and SphereGAN as representative remedies.

  • WGAN: WGAN replaces the original min-max cost with Earth Mover distance, providing measurable gradients even when real and generated distributions are far apart.The EM distance is continuous and supplies useful gradients for training the generator; WGAN’s gradient remains smooth where the original GAN cost saturates.
  • SNGAN: SNGAN stabilizes discriminator training by constraining layer weight spectra, bounding its Lipschitz constant by 1 for WGAN optimization.The method is computationally inexpensive, easy to apply to existing GAN architectures, and improves or matches quality on ImageNet, CIFAR-10, and STL-10 versus prior stabilization techniques.
  • SphereGAN: SphereGAN uses hypersphere-based integral probability metrics and geometric moment matching to enhance training stability and exploit higher-order data statistics.Defining IPMs on the hypersphere alleviates discriminator Lipschitz constraints and avoids additional constraints needed to force the discriminator into a desired function space.

6 Data augmentation with GAN

GANs can augment data when annotations, diversity, or direct access are limited. They generate annotated images, varied face stimuli, and synthetic sensitive signals to reduce reliance on costly collection or restricted databases.

  • Motivation: Data augmentation addresses limited annotations, limited diversity, and restricted data access.These conditions arise from few labeled examples, insufficient variation, or sensitive databases that cannot be accessed directly.
  • Limited annotations: SGAN generates new annotated images and automatically enriches training data when labels are scarce.Supervised alternatives require substantial human effort to add labels or use active learning.
  • Limited diversity: StyleGAN generates broad types of face images for psychology and neuroscience stimuli requiring varied emotional appearances.Traditional preparation of such stimuli is time-consuming and costly, while EEG responses can depend on face types such as happy, angry, and sad.
  • Restricted data: GANs can synthesize privacy-sensitive data, such as ECG signals that resemble real ECG data, when databases are restricted.This use case is associated with unsupervised learning approaches.

7 Conclusion

The chapter offers a concise introduction to GANs and their usages, providing tools to understand the technique and navigate its extensive literature. It also notes that many GAN variants and applications were omitted for brevity, alongside normalizing flows and score-based generative models.

  • 7 Conclusion: The chapter provides a concise overview of GANs and their various usages.The authors describe this overview as only a glimpse of the broader field.
  • 7 Conclusion: The chapter equips readers to better understand GANs and navigate the vast recent literature on the topic.The authors present the chapter’s description as a practical foundation for engaging with subsequent work.
  • 7 Conclusion: Many GAN variants and applications are omitted for brevity, while normalizing flows and score-based generative models are also identified as relevant models.The conclusion explicitly mentions these additional model families without presenting them in detail.
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