mlsanityData to 7 Oct 2026

Mode Regularized Generative Adversarial Networks

arXiv 1612.02136 ↗First posted 7 Dec 2016cs.LG cs.AI cs.CVcited by 123

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Abstract (arXiv)

Although Generative Adversarial Networks achieve state-of-the-art results on a variety of generative tasks, they are regarded as highly unstable and prone to miss modes. We argue that these bad behaviors of GANs are due to the very particular functional shape of the trained discriminators in high dimensional spaces, which can easily make training stuck or push probability mass in the wrong direction, towards that of higher concentration than that of the data generating distribution. We introduce several ways of regularizing the objective, which can dramatically stabilize the training of GAN models. We also show that our regularizers can help the fair distribution of probability mass across the modes of the data generating distribution, during the early phases of training and thus providing a unified solution to the missing modes problem.

Key facts. Cited by 123 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Dec 2016.

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Citing papers per year: 2018: 1; 2019: 1; 2020: 0; 2021: 37; 2022: 26; 2023: 22; 2024: 15; 2025: 16; 2026: 5.

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  16. 2021Conditional Frechet Inception Distancecited by 16
  17. 2024A Practical Guide to Sample-based Statistical Distances for Evaluating Generative Models in Sciencecited by 14
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