mlsanityData to 7 Oct 2026

Spectral Normalization for Generative Adversarial Networks

arXiv 1802.05957 ↗First posted 16 Feb 2018cs.LG cs.CV stat.MLcited by 1,618

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Cited by 118 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

One of the challenges in the study of generative adversarial networks is the instability of its training. In this paper, we propose a novel weight normalization technique called spectral normalization to stabilize the training of the discriminator. Our new normalization technique is computationally light and easy to incorporate into existing implementations. We tested the efficacy of spectral normalization on CIFAR10, STL-10, and ILSVRC2012 dataset, and we experimentally confirmed that spectrally normalized GANs (SN-GANs) is capable of generating images of better or equal quality relative to the previous training stabilization techniques.

Key facts. Cited by 1,618 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Feb 2018.

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Citing papers per year: 2018: 1; 2019: 2; 2020: 5; 2021: 452; 2022: 312; 2023: 296; 2024: 191; 2025: 175; 2026: 184.

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