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VARGAN: Variance Enforcing Network Enhanced GAN

arXiv 2109.02117 ↗First posted 5 Sep 2021cs.LGcited by 0

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Abstract (arXiv)

Generative adversarial networks (GANs) are one of the most widely used generative models. GANs can learn complex multi-modal distributions, and generate real-like samples. Despite the major success of GANs in generating synthetic data, they might suffer from unstable training process, and mode collapse. In this paper, we introduce a new GAN architecture called variance enforcing GAN (VARGAN), which incorporates a third network to introduce diversity in the generated samples. The third network measures the diversity of the generated samples, which is used to penalize the generator's loss for low diversity samples. The network is trained on the available training data and undesired distributions with limited modality. On a set of synthetic and real-world image data, VARGAN generates a more diverse set of samples compared to the recent state-of-the-art models. High diversity and low computational complexity, as well as fast convergence, make VARGAN a promising model to alleviate mode collapse.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 30 of its 41 references that are in the map. First posted on arXiv 5 Sep 2021.

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References · 30

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18 more
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  18. 2019Rethinking Generative Mode Coverage: A Pointwise Guaranteed Approachcited by 3

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