PyTorch Implementation of Deep Convolutional Generative Adversarial Networks (DCGAN)

This is my implementation of DCGAN (Radford & Metz, 2016) in PyTorch (Pazkea et al., 2019).

As I'm an active learning student, this implementation may not be complete or accurate. Therefore, I recommend you to use other reliable implementations if you're willing to use it in your project.

If you find any bugs or flaws, please let me know. Also, I tried using the APA style citation, just for practicing. If you think any of the citations are improper, please let me know.

Model Outputs

Training on other datasets (like LSUN (Yu, Zhang, Song, Seff, & Xiao, 2015) or CelebA (Liu, Luo, Wang, &Tang, 2015)) or on higher epochs will be performed later.

Single epoch on MNIST (LeCun, Bottou, Bengio, & Haffner, 1998)

Training MNIST (LeCun et al., 1998) for higher epoch causes generator to collapse - generating only two images. I guess this is because of the small dataset.

10 epochs on CelebA (Liu et al., 2015)

The model trained with the 64x64 sized cetercrop of aligned images.

Pretrained Models

You can download pretrained generators G-n.pt from the Models folder. n is the number of epochs used for training.

References

• LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.
• Liu, Z., Luo, P., Wang, X., & Tang, X. (2015). Deep Learning Face Attributes in the Wild. Proceedings of International Conference on Computer Vision.
• Pazke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., & Chintala, S. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. Advances in Neural Information Processing Systems 32 (pp. 8024-8035). Curran Associates, Inc.
• Radford, A., & Metz, L. (2016). Unsupervised Representational Learning with Deep Convolutional Generative Adversarial Networks. arXiv preprint. arXiv:1511.06434v2
• Yu, F., Zhang, Y., Song, S., Seff, A. & Xiao, J. (2015). LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop. arXiv preprint. arXiv:1506:03365