mlsanityData to 7 Oct 2026

Conditional Image Synthesis With Auxiliary Classifier GANs

arXiv 1610.09585 ↗First posted 30 Oct 2016stat.ML cs.CVcited by 562

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Cited by 43 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Synthesizing high resolution photorealistic images has been a long-standing challenge in machine learning. In this paper we introduce new methods for the improved training of generative adversarial networks (GANs) for image synthesis. We construct a variant of GANs employing label conditioning that results in 128x128 resolution image samples exhibiting global coherence. We expand on previous work for image quality assessment to provide two new analyses for assessing the discriminability and diversity of samples from class-conditional image synthesis models. These analyses demonstrate that high resolution samples provide class information not present in low resolution samples. Across 1000 ImageNet classes, 128x128 samples are more than twice as discriminable as artificially resized 32x32 samples. In addition, 84.7% of the classes have samples exhibiting diversity comparable to real ImageNet data.

Key facts. Cited by 562 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Oct 2016.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 189; 2022: 125; 2023: 102; 2024: 71; 2025: 57; 2026: 17.

562 papers.

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