This repository contains the code for the paper Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning.
The code is based on the excellent implementation of DCGAN.
MNIST images generated by SteinGAN.
Results on CIFAR-10. For more details, please refer to our paper.
CelebA images generated by SteinGAN.
Images generated by SteinGAN when performing a random walk on the random input; we can see that a man with glasses and black hair gradually changes to a woman with blonde hair.
If you find SteinGAN helpful for your research, please cite the following papers:
- Dilin Wang and Qiang Liu. Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning. arXiv preprint arXiv:1611.01722, 2016.
- Alec Radford, Luke Metz, Soumith Chintala. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. arXiv preprint arXiv:1511.06434. 2015.
Feedback is greatly appreciated. If you have any questions, comments, issues or anything else really, shoot us an email.
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