[1511.06434] Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

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Computer Science > Machine Learning

arXiv:1511.06434 (cs)

[Submitted on 19 Nov 2015 (v1), last revised 7 Jan 2016 (this version, v2)]

Title:Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Authors:Alec Radford, Luke Metz, Soumith Chintala<br>View a PDF of the paper titled Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, by Alec Radford and 2 other authors

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Abstract: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.

Comments:<br>Under review as a conference paper at ICLR 2016

Subjects:

Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)

Cite as:<br>arXiv:1511.06434 [cs.LG]

(or<br>arXiv:1511.06434v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.1511.06434

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arXiv-issued DOI via DataCite

Submission history<br>From: Alec Radford [view email]<br>[v1]<br>Thu, 19 Nov 2015 22:50:32 UTC (9,027 KB)

[v2]<br>Thu, 7 Jan 2016 23:09:39 UTC (9,028 KB)

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