Data to 14 Oct 2026

VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning

arXiv 1705.07761 ↗First posted 22 May 2017stat.MLcited by 200

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Cited by 31 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Deep generative models provide powerful tools for distributions over complicated manifolds, such as those of natural images. But many of these methods, including generative adversarial networks (GANs), can be difficult to train, in part because they are prone to mode collapse, which means that they characterize only a few modes of the true distribution. To address this, we introduce VEEGAN, which features a reconstructor network, reversing the action of the generator by mapping from data to noise. Our training objective retains the original asymptotic consistency guarantee of GANs, and can be interpreted as a novel autoencoder loss over the noise. In sharp contrast to a traditional autoencoder over data points, VEEGAN does not require specifying a loss function over the data, but rather only over the representations, which are standard normal by assumption. On an extensive set of synthetic and real world image datasets, VEEGAN indeed resists mode collapsing to a far greater extent than other recent GAN variants, and produces more realistic samples.

Key facts. Cited by 200 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 May 2017.

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Citing papers per year: 2018: 1; 2019: 0; 2020: 0; 2021: 46; 2022: 44; 2023: 41; 2024: 30; 2025: 27; 2026: 11.

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2022The Vendi Score: A Diversity Evaluation Metric for Machine Learningcited by 311
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2023CoDi: Co-evolving Contrastive Diffusion Models for Mixed-type Tabular Synthesiscited by 78

38 more

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2024Copyright Protection in Generative AI: A Technical Perspectivecited by 38
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