Capsule research with our trivial contribution
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Updated
Aug 14, 2018 - Python
Capsule research with our trivial contribution
Tensorflow Implementation of "Large-scale Optimal Transport and Mapping Estimation"(ICLR2018/NIPS 2017 OTML)
Implementation of the Sliced Wasserstein Autoencoder using PyTorch
Continuous-time gradient flow for generative modeling and variational inference
Wasserstein barycenter research for images
Unofficial PyTorch implementation of "Progressive Growing of GANs for Improved Quality, Stability, and Variation".
Neural Architecture Search with Bayesian Optimisation and Optimal Transport
Entropy-regularized Optimal Transport Generative Models
FML (Francis' Machine-Learnin' Library) - A collection of utilities for machine learning tasks
Implementation of DeepJDOT in Keras
Unofficial PyTorch implementation of "GAN-QP: A Novel GAN Framework without Gradient Vanishing and Lipschitz Constraint"
Tensorflow implementation of optimal transport (OT) with Sinkhorn algorithm.
An implementation of Wasserstein Fair Classification, a conference paper submitted to UAI 2019.
Code for NeurIPS 2019 paper "Screening Sinkhorn Algorithm for Regularized Optimal Transport"
contains programs and data to benchmark data matching methods
NumPy implementations of recent Optimal Transport algorithms
Pytorch implementation for "Enhanced Transport Distance for Unsupervised Domain Adaptation" (ETD) (CVPR 2020)
The Wasserstein Distance and Optimal Transport Map of Gaussian Processes
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