yuntaodu

TOEFL_laylalaisy

备考托福的一丢丢经验+资料~祝小可爱和大佬们都早日和托福大魔王分手(o゜▽゜)o☆

3CATN

Cycle-consistent Conditional Adversarial Transfer Networks, ACM MM 2019

BNM

code of Towards Discriminability and Diversity: Batch Nuclear-norm Maximization under Label Insufficient Situations (CVPR2020 oral)

CycleGAN3D

Unsupervised Video-to-Video Translation

GVB

Code of Gradually Vanishing Bridge for Adversarial Domain Adaptation (CVPR2020)

HADDA

Heterogeneous Adversarial Deep Domain Adaptation

LabelNoiseCorrection

Official Implementation of ICML 2019 Unsupervised label noise modeling and loss correction

MMT

[ICLR-2020] Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification.

PENCIL

PyTorch implementation of Probabilistic End-to-end Noise Correction for Learning with Noisy Labels, CVPR 2019.

Prototype-Matching-Graph-Network-for-Heterogeneous-Domain-Adaptation

RevGAN

RevGAN implementation in PyTorch. We extend the Pix2pix and CycleGAN framework by exploring approximately invertible architectures in 2D and 3D. These architectures are approximately invertible by design and thus partially satisfy cycle-consistency before training even begins. Furthermore, since invertible architectures have constant memory complexity in depth, these models can be built arbitrarily deep without requiring additional memory. In the paper we demonstrate superior quantitative output on the Cityscapes and Maps datasets at near constant memory budget.

SLSA-DA

Sparsely-Labeled Source Assisted Domain Adaptation

ssl_bad_gan

Good Semi-Supervised Learning That Requires a Bad GAN

tsn-pytorch

Temporal Segment Networks (TSN) in PyTorch