FairnessTrustworthinessBiasstat.ML
Parallelly Tempered Generative Adversarial Nets: Toward Stabilized Gradients
Jinwon Sohn, Qifan Song
November 18, 2024
Key Takeaways
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GANs struggle with "mode collapse," where the AI fails to learn the full diversity of data patterns. The paper identifies that this training instability stems from the complexity of learning from multimodal (highly varied) data distributions, which causes unreliable gradient estimates during training.
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The proposed "parallel tempering" approach helps GANs learn more stable and complete representations by training on multiple simplified versions of the target data simultaneously. This method reduces the variance in gradient calculations, making the training process more reliable and efficient across both image and tabular datasets.
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The framework has direct applications for responsible AI, including the ability to generate fair synthetic data. By addressing training instability, the approach creates opportunities to develop synthetic datasets that can help mitigate bias in AI systems—an important consideration for trustworthy AI development.
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Improved training stability translates to more reliable AI model development. More predictable and efficient GAN training means developers can better control and understand how generative models behave, reducing unexpected failures or unwanted outputs in real-world applications.
Abstract
A generative adversarial network (GAN) has been a representative backbone model in generative artificial intelligence (AI) because of its powerful performance in capturing intricate data-generating processes. However, the GAN training is well-known for its notorious training instability, usually characterized by the occurrence of mode collapse. Through the lens of gradients' variance, this work particularly analyzes the training instability and inefficiency in the presence of mode collapse by linking it to multimodality in the target distribution. To ease the raised training issues from severe multimodality, we introduce a novel GAN training framework that leverages a series of tempered distributions produced via convex interpolation. With our newly developed GAN objective function, the generator can learn all the tempered distributions simultaneously, conceptually resonating with the parallel tempering in statistics. Our simulation studies demonstrate the superiority of our approach over existing popular training strategies in both image and tabular data synthesis. We theoretically analyze that such significant improvement can arise from reducing the variance of gradient estimates by using the tempered distributions. Finally, we further develop a variant of the proposed framework aimed at generating fair synthetic data which is one of the growing interests in the field of trustworthy AI.