mikigom / large-scale-OT-mapping-TF
Tensorflow Implementation of "Large-scale Optimal Transport and Mapping Estimation"(ICLR2018/NIPS 2017 OTML)
☆19Updated 6 years ago
Alternatives and similar repositories for large-scale-OT-mapping-TF:
Users that are interested in large-scale-OT-mapping-TF are comparing it to the libraries listed below
- Stochastic algorithms for computing Regularized Optimal Transport☆57Updated 6 years ago
- Scaled MMD GAN☆36Updated 5 years ago
- Sliced Wasserstein Generator☆37Updated 6 years ago
- Stochastic Optimization for Optimal Transport☆22Updated 8 years ago
- Sliced Wasserstein Distance for Learning Gaussian Mixture Models☆62Updated last year
- Joint distribution optimal transportation for domain adaptation☆100Updated 7 years ago
- Sliced Wasserstein Generator☆23Updated 6 years ago
- Gabriel Peyré, Marco Cuturi, Justin Solomon, Gromov-Wasserstein Averaging of Kernel and Distance Matrices, Proc. of ICML 2016.☆73Updated 8 years ago
- Implementation of the Sliced Wasserstein Autoencoders☆91Updated 6 years ago
- Gaussian Process Prior Variational Autoencoder☆83Updated 6 years ago
- Implementation of the Sliced Wasserstein Autoencoder using PyTorch☆101Updated 6 years ago
- ☆28Updated 3 years ago
- MMD, Hausdorff and Sinkhorn divergences scaled up to 1,000,000 samples.☆55Updated 5 years ago
- Code for http://proceedings.mlr.press/v80/dvurechensky18a.html☆16Updated 6 years ago
- A matlab toolbox to perform Wasserstein Dictionary Learning or NMF☆32Updated 8 years ago
- Learning generative models with Sinkhorn Loss☆28Updated 6 years ago
- demo codes for several approximate optimal transport solvers☆20Updated 6 years ago
- L. Chizat, G. Peyré, B. Schmitzer, F-X. Vialard. Scaling Algorithms for Unbalanced Transport Problems. Preprint Arxiv:1607.05816, 2016.☆42Updated 8 years ago
- Code for Sliced Gromov-Wasserstein☆67Updated 5 years ago
- Learning Generative Models across Incomparable Spaces (ICML 2019)☆27Updated 5 years ago
- This is the code for our paper: Semi-Supervised Learning With GANs: Revisiting Manifold Regularization (ICLR 2018)☆44Updated 5 years ago
- ☆12Updated 6 years ago
- Scalable Training of Inference Networks for Gaussian-Process Models, ICML 2019☆41Updated 2 years ago
- Code for "Differentiable Compositional Kernel Learning for Gaussian Processes" https://arxiv.org/abs/1806.04326☆71Updated 6 years ago
- Code for "How to Train Deep Variational Autoencoders and Probabilistic Ladder Networks"☆100Updated 8 years ago
- Code for "A Spectral Approach to Gradient Estimation for Implicit Distributions" (ICML'18)☆33Updated last year
- A variational inference method with accurate uncertainty estimation. It uses a new semi-implicit variational family built on neural netwo…☆53Updated 5 months ago
- Riemannian approach to batch normalization☆21Updated 7 years ago
- Nonlinear SVGD for Learning Diversified Mixture Models☆13Updated 6 years ago
- Pytorch version of "Deep Convolutional Networks as shallow Gaussian Processes" by Adrià Garriga-Alonso, Carl Rasmussen and Laurence Aitch…☆32Updated 4 years ago