mlsanityData to 14 Oct 2026

Unrolled Generative Adversarial Networks

arXiv 1611.02163 ↗First posted 7 Nov 2016cs.LG stat.MLcited by 208

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Cited by 37 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We introduce a method to stabilize Generative Adversarial Networks (GANs) by defining the generator objective with respect to an unrolled optimization of the discriminator. This allows training to be adjusted between using the optimal discriminator in the generator's objective, which is ideal but infeasible in practice, and using the current value of the discriminator, which is often unstable and leads to poor solutions. We show how this technique solves the common problem of mode collapse, stabilizes training of GANs with complex recurrent generators, and increases diversity and coverage of the data distribution by the generator.

Key facts. Cited by 208 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Nov 2016.

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Citing papers per year: 2018: 1; 2019: 0; 2020: 0; 2021: 71; 2022: 46; 2023: 45; 2024: 24; 2025: 12; 2026: 9.

208 papers.

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