luethan2025/dcgan

Quick Start

git clone https://github.com/bareform/dcgan.git
cd dcgan

You can download the pre-trained models here and use the provided Jupyter Notebook inference.ipynb to generate some samples.

Method

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Alec Radford¹, Luke Metz¹, Soumith Chintala²

¹indico Research, ²Facebook AI Research

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.

Citation

The original paper can be found at:

@misc{radford2015dcgan,
    title={Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks},
    author={Alec Radford and Luke Metz and Soumith Chintala},
    year={2015},
    eprint={1511.06434},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}