Open-source alternatives to WBC Classification UDA

Learning to Cluster. A deep clustering strategy.

Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling

Code release for Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering (CVPR2020-Oral).

This is the implementation of Partially-Shared Variational Auto Encoders (PS-VAEs) for pose estimation and digits classification in Pytorch. The code is written by Ryuhei Takahashi and Atsushi Hashimoto. The work was accepted by ECCV 2020 Poster.

(ICCV'19 Best Paper Nomination) Larger Norm More Transferable: An Adaptive Feature Norm Approach for Unsupervised Domain Adaptation

pytorch implementation for Contrastive Adaptation Network

Implemenation of Asymmetric-TriTraining by Tensorflow

PyTorch implementation of AdaGraph: Unifying Predictive and Continuous Domain Adaptation through Graphs

Code for the paper "Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation", ICLR 2018

Code for paper "Unsupervised Domain Adaptation using Feature-Whitening and Consensus Loss" (CVPR 2019)

Prior Knowledge Guided Unsupervised Domain Adaptation (ECCV 2022)

Safe Self-Refinement for Transformer-based Domain Adaptation (CVPR 2022)

Official Implementation of "Domain Specific Batch Normalization for Unsupervised Domain Adaptation (CVPR2019)"

Code for Switchable Whitening (ICCV2019)

Code for paper "Spherical space domain adaptation with pseudo label (CVPR 2020)" and "Unsupervised and Semi-supervised Robust Spherical Space Domain Adaptation (TPAMI 2022)".

Code for Virtual Mixup Training for Unsupervised Domain Adaptation.

A2LP for short, ECCV2020 spotlight, Investigating SSL principles for UDA problems

Release code for light-weight calibrator: a separable component for unsupervised domain adaptation

Code for in ICCV19 (Oral)

This is the source code of our proposed method ICCV2023 paper "Towards effective instance discrimination contrastive loss for unsupervised domain adaptation".