DCGAN-PyTorch

Overview

This repository contains an op-for-op PyTorch reimplementation of Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks .

Table of contents

• DCGAN-PyTorch
  • Overview
    • Table of contents
    • Download weights
    • Test
    • Train
    • Contributing
    • Credit
      • Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Download weights

• Google Driver
• Baidu Driver access:llot

Test

Modify the contents of the file as follows.

1. config.py line 35 mode="train" change to model="valid";
2. config.py line 83 model_path=f"results/{exp_name}/g-last.pth" change to model_path=f"<YOUR-WEIGHTS-PATH>.pth";
3. Run python validate.py.

Train

Modify the contents of the file as follows.

1. config.py line 35 mode="valid" change to model="train";
2. Run python train.py.

If you want to load weights that you've trained before, modify the contents of the file as follows.

1. config.py line 35 mode="valid" change to model="train";
2. config.py line 52 start_epoch=0 change to start_epoch=XXX;
3. config.py line 53 resume=False change to resume=True;
4. config.py line 54 resume_d_weight="" change to resume_d_weight=<YOUR-RESUME-D-WIGHTS-PATH>;
5. config.py line 55 resume_g_weight="" change to resume_g_weight=<YOUR-RESUME-G-WIGHTS-PATH>;
6. Run python train.py.

Contributing

If you find a bug, create a GitHub issue, or even better, submit a pull request. Similarly, if you have questions, simply post them as GitHub issues.

I look forward to seeing what the community does with these models!

Credit

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Alec Radford, Luke Metz, Soumith Chintala

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.

[Paper]

@article{adversarial,
  title={Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks},
  author={Alec Radford, Luke Metz, Soumith Chintala},
  journal={arXiv},
  year={2015}
}