mlsanityData to 14 Oct 2026

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

arXiv 1511.06434 ↗First posted 19 Nov 2015cs.LG cs.CVcited by 2,054

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Cited by 157 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications. Comparatively, unsupervised learning with CNNs has received less attention. In this work we hope to help bridge the gap between the success of CNNs for supervised learning and unsupervised learning. We introduce a class of CNNs called deep convolutional generative adversarial networks (DCGANs), that have certain architectural constraints, and demonstrate that they are a strong candidate for unsupervised learning. Training on various image datasets, we show convincing evidence that our deep convolutional adversarial pair learns a hierarchy of representations from object parts to scenes in both the generator and discriminator. Additionally, we use the learned features for novel tasks - demonstrating their applicability as general image representations.

Key facts. Cited by 2,054 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Nov 2015.

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Citing papers per year: 2018: 4; 2019: 0; 2020: 5; 2021: 634; 2022: 480; 2023: 394; 2024: 258; 2025: 188; 2026: 91.

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