|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Transferlearning||11,975||3 days ago||8||mit||Python|
|Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习|
|Awesome Domain Adaptation||4,554||17 days ago||2||mit|
|A collection of AWESOME things about domian adaptation|
|Awesome Transfer Learning||1,539||a year ago||7|
|Best transfer learning and domain adaptation resources (papers, tutorials, datasets, etc.)|
|Eanet||374||3 years ago||19||Python|
|EANet: Enhancing Alignment for Cross-Domain Person Re-identification|
|Transfer Learning Materials||227||a month ago||apache-2.0||Python|
|resource collection for transfer learning!|
|Adaptationseg||116||3 years ago||11||Python|
|Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes, ICCV 2017|
|Contrastive Adaptation Network For Unsupervised Domain Adaptation||108||3 years ago||11||apache-2.0||Python|
|pytorch implementation for Contrastive Adaptation Network|
|Dmenet||89||a year ago||agpl-3.0||Python|
|[CVPR 2019] Official TensorFlow Implementation for "Deep Defocus Map Estimation using Domain Adaptation"|
|Awesome Artificial Intelligence Research||80||10 months ago|
|A curated list of Artificial Intelligence (AI) Research, tracks the cutting edge trending of AI research, including recommender systems, computer vision, machine learning, etc.|
|Deep Transfer Learning||71||a year ago|
|Deep Transfer Learning Papers|
A list of awesome papers and cool resources on transfer learning, domain adaptation and domain-to-domain translation in general! As you will notice, this list is currently mostly focused on domain adaptation (DA) and domain-to-domain translation, but don't hesitate to suggest resources in other subfields of transfer learning.
Note: this list is not actively maintained anymore, but I still accept pull requests, so please don't hesitate if you want to contribute with newer resources
Papers are ordered by theme and inside each theme by publication date (submission date for arXiv papers). If the network or algorithm is given a name in a paper, this one is written in bold before the paper's name.
Transfer of deep learning models.
Transfer between a source and a target domain. In unsupervised domain adaptation, only the source domain can have labels.
All the source points are labelled, but only few target points are.
Only a few target examples are available, but they are labelled
Domain adaptation applied to other fields
The results are indicated as the prediction accuracy (in %) in the target domain after adapting the source to the target. For the moment, they only correspond to the results given in the original papers, so the methodology may vary between each paper and these results must be taken with a grain of salt.