Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

@article{Radford2016UnsupervisedRL,
  title={Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks},
  author={Alec Radford and Luke Metz and Soumith Chintala},
  journal={CoRR},
  year={2016},
  volume={abs/1511.06434}
}
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… CONTINUE READING

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