Code for training Generative Adversarial Networks (GANs), and evaluating the models' mode collapse. The code was taken and adapted from https://github.com/carpedm20/DCGAN-tensorflow .
- DCGAN (https://arxiv.org/pdf/1511.06434.pdf)
- ALI (Adversarially Learned Inference, https://arxiv.org/pdf/1606.00704.pdf)
- Unrolled GAN (https://arxiv.org/pdf/1611.02163.pdf)
Evaluating mode collapse using the following 2 methods, introduced in https://arxiv.org/pdf/1611.02163.pdf :
- Number of modes covered with the Stacked Mnist dataset (section 3.3.1, Discrete Mode Collapse)
- Inference via Optimisation (section 3.4.1)
- Tensorflow
- SciPy
- Keras (only used to import keras.optimizers.Adam )
- Stacked MNIST - can be created by running StackedMNIST_data_setup.py
- CIFAR10 - download the python version from http://www.cs.toronto.edu/~kriz/cifar.html and place the content in /data/cifar10
The Run_*.py files contain preset configurations for each model/dataset.