Implementation of implicit generative models based on the MMD in PyTorch (for Python 3.6+). Very much a work in progress; feel free to get in touch if you want to use it.
Currently contains:
igms.featurize: extract features of images from pretrained classifiers (torchvisionor the pretrained L2-robust ones fromlocuslab/smoothing).igms.kernels: various standard kernels, potentially on top of those features, with some fancy machinery to cache various sums and so on.igms.mmdto estimate the MMD, run permutation tests based on the MMD, and unbiasedly estimate the variance of the MMD, from theigms.kernels.LazyKernelPairclass.train_gmmn.py: train a Generative Moment Matching Networks (Y. Li+ ICML-15 / Dziugaite+ UAI-15), i.e. just minimize the MMD with a fixed kernel between the model and the target distribution, but using much richer kernels than those papers used.
Soon:
- Generative Feature Matching Networks (Santos+ 2019), which are basically GMMNs with a moving-average trick to estimate the MMD.
- FID and KID evaluation (KID is essentially done, just need a wrapper).
- MMD three-sample tests like Bounliphone+ ICLR-16 (essentially done).
- Optimized kernel two-sample tests like Sutherland+ ICLR-17 – not really an IGM, but it's basically implemented already.
- Adaptive IGM learning rates based on the KID three-sample test, like in Bińkowski+ ICLR-18.
Other things I might implement here eventually:
- (S)MMD-GANs: C.-L. Li+ NIPS-17 / Bińkowski+ ICLR-18 / Arbel+ NeurIPS-18, which use a GAN-like adversarial game to choose the best kernel.
- Method of Learned Moments (Ravuri+ ICML-18), which is like a GMMN that (a) uses gradient features and (b) occasionally updates them based on retraining the classifier.
- Generative Latent Optimization (Bojanowski+ 2017)?