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mnist_center_loss_pytorch

A Pytorch implementation of center loss on MNIST

new-pac

科学/自由上网，免费ss/ssr/v2ray/goflyway账号，搭建教程

noisy_labels

TRAINING DEEP NEURAL-NETWORKS USING A NOISE ADAPTATION LAYER

oneshottranslation

Pytorch implementation of "One-Shot Unsupervised Cross Domain Translation" NIPS 2018

opda_bp

openset-da

Code for 'Open Set Domain Adaptation by Backpropagation'

otml_statlearn2018

Courses and practical sessions for the Optimal Transport and Machine learning course at Statlearn 2018

pada

Code release for "Partial Adversarial Domain Adaptation" (ECCV 2018)

person-reid-triplet-loss

Person re-ID baseline with triplet loss

personae

📈 Personae is a repo of implements and environment of Deep Reinforcement Learning & Supervised Learning for Quantitative Trading.

pot

Python Optimal Transport library

prf

Probabilistic Random Forest: A machine learning algorithm for noisy datasets

probabilistic-unet-pytorch

A Probabilistic U-Net for segmentation of ambiguous images implemented in PyTorch

probabilistic_unet

A U-Net combined with a variational auto-encoder that is able to learn conditional distributions over semantic segmentations.

probabilistic_unet-1

A U-Net combined with a variational auto-encoder that is able to learn conditional distributions over semantic segmentations.

pwc

Papers with code. Sorted by stars. Updated weekly.

python

All Algorithms implemented in Python

pytorch-bayesiancnn

Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.

pytorch-center-loss

Pytorch implementation of Center Loss

pytorch-cyclegan

A clean and readable Pytorch implementation of CycleGAN

pytorch-cyclegan-and-pix2pix

Image-to-image translation in PyTorch (e.g., horse2zebra, edges2cats, and more)

pytorch-maml

PyTorch implementation of MAML: https://arxiv.org/abs/1703.03400

rmi

This is the code for the NeurIPS 2019 paper Region Mutual Information Loss for Semantic Segmentation.

s2cnn

Spherical CNNs

selectiongan

Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation (CVPR 2019 Oral)

sfda

Source-Free Domain Adaptation

sfda-dpl

[MICCAI'21] Source-Free domain adaptive fundus image segmentation with denoised pseudo-labeling

sfda_neighbors

Code for our NeurIPS 2021 paper 'Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation'

shap

A unified approach to explain the output of any machine learning model.

simpleselfattention