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This project is a comprehensive library for transfer learning and domain adaptation in computer vision. It serves as a framework for aligning feature distributions between source and target datasets, a toolkit for domain generalization, and a library for semi-supervised learning using small labeled datasets and large unlabeled sets. The library provides specialized capabilities for unsupervised domain adaptation, including the use of adversarial networks, discrepancy-based architectures, and image-to-image translation to reduce distribution mismatch. It also includes tools for domain generali
Code of Gradually Vanishing Bridge for Adversarial Domain Adaptation (CVPR2020)
Official implementation for SPA: A Graph Spectral Alignment Perspective for Domain Adaptation (NeurIPS 2023)
Official implementation of Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation presented at ICCV 2019.
The main features of postbg/dta.pytorch are: Adversarial Adaptation Methods.
Open-source alternatives to postbg/dta.pytorch include: thuml/transfer-learning-library — This project is a comprehensive library for transfer learning and domain adaptation in computer vision. It serves as a… cuishuhao/gvb — Code of Gradually Vanishing Bridge for Adversarial Domain Adaptation (CVPR2020). ddtm/caffe — Caffe: a fast open framework for deep learning. dmirlab-group/dsr — The implement of "Learning Disentangled Semantic Representation for Domain Adaptation" (IJCAI 2019). engharat/sbadagan — SBADA-GAN CVPR 2018 code This is a preliminary release, as the code needs a massive cleanup being extremely verbose in… crownx/spa — Official implementation for SPA: A Graph Spectral Alignment Perspective for Domain Adaptation (NeurIPS 2023).
