Regularizing Generative Adversarial Networks under Limited Data (2104.03310v1)

Abstract: Recent years have witnessed the rapid progress of generative adversarial networks (GANs). However, the success of the GAN models hinges on a large amount of training data. This work proposes a regularization approach for training robust GAN models on limited data. We theoretically show a connection between the regularized loss and an f-divergence called LeCam-divergence, which we find is more robust under limited training data. Extensive experiments on several benchmark datasets demonstrate that the proposed regularization scheme 1) improves the generalization performance and stabilizes the learning dynamics of GAN models under limited training data, and 2) complements the recent data augmentation methods. These properties facilitate training GAN models to achieve state-of-the-art performance when only limited training data of the ImageNet benchmark is available.

• The paper proposes a novel regularization method using L2 norm and exponential moving averages to train GANs effectively with limited data.
• The method connects the regularized loss to the LeCam-divergence, providing theoretical grounding for improved stability under data constraints.
• Empirical results show improved FID scores on standard datasets, particularly under low data conditions, and compatibility with state-of-the-art data augmentation.

Regularizing Generative Adversarial Networks under Limited Data: An In-Depth Analysis

In the field of generative adversarial networks (GANs), the reliance on large datasets for training remains a substantial obstacle, particularly in applications where such quantities of data are not readily accessible. This paper proposes a novel regularization approach to enhance the performance of GAN models when confronted with limited data, a crucial contribution to the ongoing advancement of generative model training.

Key Contributions and Methodology

The paper's central thesis is the introduction of a regularization method specifically tailored for scenarios with limited data. By focusing on model regularization—distinctly from more prevalent data augmentation methods—it aims to bolster the generalization capabilities of GANs. The proposed method introduces an $\ell_2$ norm-based regularizer which aligns the discriminator's prediction through exponential moving averages (EMAs) of both real and generated data, hence improving overall robustness and stability.

A pivotal theoretical insight is the connection drawn between the regularized loss function and the LeCam-divergence, a type of $f$-divergence known for robustness under limited data conditions. By integrating this divergence into the GAN's training regimen, the authors argue for a fundamentally more stable learning process under data constraints. This theoretical grounding is backed by empirical results demonstrating improved performance over both established and recent alternative methods.

Numerical Results and Empirical Validation

The experimental section offers an extensive evaluation through multiple standard datasets, namely CIFAR-10, CIFAR-100, and ImageNet. In all these domains, the proposed method consistently yields better Frechet Inception Distance (FID) scores compared with traditional models, such as BigGAN. Notably, it exhibits significant performance improvements under stringent data availability constraints, as low as 10% of the full datasets. Additionally, when combined with current state-of-the-art data augmentation techniques—such as Differentiable Augmentation (DA) and Adaptive Data Augmentation (ADA)—the regularization complements and, in many cases, amplifies their effects.

Implications of Regularization in GAN Training

Practically, this paper's contributions have broad implications for the training of GANs in scenarios where data is limited. Training GANs without compromising on the quality of generated content can dramatically increase the applicability of these models in fields like personalized content generation and low-resource domain adaptation. Theoretically, anchoring regularization in the LeCam-divergence opens new horizons for evaluating GAN performance under data scarcity, driving further refinements in divergence metrics suitable for machine learning applications.

Speculation on Future Directions

Looking forward, the establishment of a regularization method underpinned by theoretical insights presents exciting opportunities. Future work could explore adaptive tuning of the regularization strength, potentially leading to model-independent schemes where the degree of regularization dynamically adjusts with data availability and quality. Additionally, evaluating this approach in synergy with more sophisticated GAN architectures and non-conventional data types might even further its applicability and robustness.

Conclusion

In sum, this paper provides a thought-provoking perspective on tackling the training of GANs with limited data availability, an issue increasingly prevalent in today's data-driven landscape. Through the combination of theoretical rigor and empirical validation, this paper sets a precedent for future explorations into GAN regularization strategies, highlighting a meaningful path toward resolving data scarcity challenges in generative modeling.