- NeurIPS'22 Respecting Transfer Gap in Knowledge Distillation [[arxiv](http://arxiv.org/abs/2210.12787)]
-Transfer gap in distillation 知识蒸馏中的迁移gap
- NeurIPS'22 Domain Generalization without Excess Empirical Risk [[openreview](https://openreview.net/forum?id=pluyPFTiTeJ)]
-Domain generalization without excess empirical risk
-Transfer of Machine Learning Fairness across Domains [[arxiv](http://arxiv.org/abs/1906.09688)]
- Fairness transfer in transfer learning 迁移学习中的公平性迁移
-NeurIPS'22 FedSR: A Simple and Effective Domain Generalization Method for Federated Learning [[openreview](https://openreview.net/forum?id=mrt90D00aQX)]
- FedSR for federated learning domain generalization 用于联邦学习的domain generalization
-On Fine-Tuned Deep Features for Unsupervised Domain Adaptation [[arxiv](http://arxiv.org/abs/2210.14083)]
-Fine-tuned features for domain adaptation 微调的特征用于域自适应
-NeurIPS'22 Probable Domain Generalization via Quantile Risk Minimization [[openreview](https://openreview.net/forum?id=6FkSHynJr1)]
-Domain generalization with quantile risk minimization 用quantile风险最小化的domain generalization
*Updated at 2022-10-24:*
- NeurIPS'22 Beyond Not-Forgetting: Continual Learning with Backward Knowledge Transfer [[arxiv](http://arxiv.org/abs/2211.00789)]
- Continual learning with backward knowledge transfer 反向知识迁移的持续学习
-WACV-23 ConfMix: Unsupervised Domain Adaptation for Object Detection via Confidence-based Mixing [[arxiv](https://arxiv.org/abs/2210.11539)]
-Domain adaptation for object detection using confidence mixing 用置信度mix做domain adaptation
-NeurIPS'22 Test Time Adaptation via Conjugate Pseudo-labels [[openreview](https://openreview.net/forum?id=2yvUYc-YNUH)]
-Test-time adaptation with conjugate pseudo-labels 用伪标签进行测试时adaptation
- CVPR-20 Regularizing CNN Transfer Learning With Randomised Regression [[arxiv](https://openaccess.thecvf.com/content_CVPR_2020/html/Zhong_Regularizing_CNN_Transfer_Learning_With_Randomised_Regression_CVPR_2020_paper.html)]
- Using randomized regression to regularize CNN 用随机回归约束CNN迁移学习
- AAAI-21 TransTailor: Pruning the Pre-trained Model for Improved Transfer Learning [[arxiv](https://ojs.aaai.org/index.php/AAAI/article/view/17046)]
- Pruning pre-trained model for transfer learning 通过对预训练模型进行剪枝来进行迁移学习
- PhDthesis Generalizing in the Real World with Representation Learning [[arxiv](http://arxiv.org/abs/2210.09925)]
- A phd thesis about generalization in real world 一篇关于现实世界如何做Generalization的博士论文
*Updated at 2022-10-17:*
- The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning [[arxiv](https://openreview.net/forum?id=Qs3EfpieOh)]
- Evolution of OOD robustness by fine-tuning
- Visual Prompt Tuning for Test-time Domain Adaptation [[arxiv](http://arxiv.org/abs/2210.04831)]
- VPT for test-time adaptation 用prompt tuning进行test-time DA
*Updated at 2022-10-10:*
- Unsupervised Domain Adaptation for COVID-19 Information Service with Contrastive Adversarial Domain Mixup [[arxiv](https://arxiv.org/abs/2210.03250)]
- Domain adaptation for COVID-19 用DA进行COVID-19预测
- ICONIP'22 IDPL: Intra-subdomain adaptation adversarial learning segmentation method based on Dynamic Pseudo Labels [[arxiv](https://arxiv.org/abs/2210.03435)]
- Intra-domain adaptation for segmentation 子领域对抗Adaptation
@@ -637,6 +637,9 @@ Here, we list some papers by topic. For list by date, please refer to [papers by
## Deep domain adaptation
- NeurIPS'22 Test Time Adaptation via Conjugate Pseudo-labels [[openreview](https://openreview.net/forum?id=2yvUYc-YNUH)]
- Test-time adaptation with conjugate pseudo-labels 用伪标签进行测试时adaptation
- On Fine-Tuned Deep Features for Unsupervised Domain Adaptation [[arxiv](http://arxiv.org/abs/2210.14083)]
- Fine-tuned features for domain adaptation 微调的特征用于域自适应
...
...
@@ -1597,6 +1600,18 @@ Here, we list some papers by topic. For list by date, please refer to [papers by
### Papers
- NeurIPS'22 Domain Generalization without Excess Empirical Risk [[openreview](https://openreview.net/forum?id=pluyPFTiTeJ)]
- Domain generalization without excess empirical risk
- NeurIPS'22 FedSR: A Simple and Effective Domain Generalization Method for Federated Learning [[openreview](https://openreview.net/forum?id=mrt90D00aQX)]
- FedSR for federated learning domain generalization 用于联邦学习的domain generalization
- NeurIPS'22 Probable Domain Generalization via Quantile Risk Minimization [[openreview](https://openreview.net/forum?id=6FkSHynJr1)]
- Domain generalization with quantile risk minimization 用quantile风险最小化的domain generalization
- NeurIPS'22 Your Out-of-Distribution Detection Method is Not Robust! [[openreview](https://openreview.net/forum?id=YUEP3ZmkL1)]
- OOD models are not robust 分布外泛化模型不够鲁棒
- PhDthesis Generalizing in the Real World with Representation Learning [[arxiv](http://arxiv.org/abs/2210.09925)]
- A phd thesis about generalization in real world 一篇关于现实世界如何做Generalization的博士论文
...
...
@@ -2378,6 +2393,9 @@ Here, we list some papers by topic. For list by date, please refer to [papers by
## Lifelong transfer learning
- NeurIPS'22 Beyond Not-Forgetting: Continual Learning with Backward Knowledge Transfer [[arxiv](http://arxiv.org/abs/2211.00789)]
- Continual learning with backward knowledge transfer 反向知识迁移的持续学习
-[Mixture of basis for interpretable continual learning with distribution shifts](https://arxiv.org/abs/2201.01853)
Here, we list some papers related to transfer learning by date (starting from 2021-07). For papers older than 2021-07, please refer to the [papers by topic](awesome_paper.md), which contains more papers.
-[Awesome papers by date](#awesome-papers-by-date)
-[2022-10](#2022-10)
-[2022-09](#2022-09)
-[2022-08](#2022-08)
-[2022-07](#2022-07)
...
...
@@ -19,6 +20,56 @@ Here, we list some papers related to transfer learning by date (starting from 20
-[2021-08](#2021-08)
-[2021-07](#2021-07)
## 2022-10
- NeurIPS'22 Respecting Transfer Gap in Knowledge Distillation [[arxiv](http://arxiv.org/abs/2210.12787)]
- Transfer gap in distillation 知识蒸馏中的迁移gap
- Transfer of Machine Learning Fairness across Domains [[arxiv](http://arxiv.org/abs/1906.09688)]
- Fairness transfer in transfer learning 迁移学习中的公平性迁移
- On Fine-Tuned Deep Features for Unsupervised Domain Adaptation [[arxiv](http://arxiv.org/abs/2210.14083)]
- Fine-tuned features for domain adaptation 微调的特征用于域自适应
- WACV-23 ConfMix: Unsupervised Domain Adaptation for Object Detection via Confidence-based Mixing [[arxiv](https://arxiv.org/abs/2210.11539)]
- Domain adaptation for object detection using confidence mixing 用置信度mix做domain adaptation
- CVPR-20 Regularizing CNN Transfer Learning With Randomised Regression [[arxiv](https://openaccess.thecvf.com/content_CVPR_2020/html/Zhong_Regularizing_CNN_Transfer_Learning_With_Randomised_Regression_CVPR_2020_paper.html)]
- Using randomized regression to regularize CNN 用随机回归约束CNN迁移学习
- AAAI-21 TransTailor: Pruning the Pre-trained Model for Improved Transfer Learning [[arxiv](https://ojs.aaai.org/index.php/AAAI/article/view/17046)]
- Pruning pre-trained model for transfer learning 通过对预训练模型进行剪枝来进行迁移学习
- PhDthesis Generalizing in the Real World with Representation Learning [[arxiv](http://arxiv.org/abs/2210.09925)]
- A phd thesis about generalization in real world 一篇关于现实世界如何做Generalization的博士论文
- The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning [[arxiv](https://openreview.net/forum?id=Qs3EfpieOh)]
- Evolution of OOD robustness by fine-tuning
- Visual Prompt Tuning for Test-time Domain Adaptation [[arxiv](http://arxiv.org/abs/2210.04831)]
- VPT for test-time adaptation 用prompt tuning进行test-time DA
- Unsupervised Domain Adaptation for COVID-19 Information Service with Contrastive Adversarial Domain Mixup [[arxiv](https://arxiv.org/abs/2210.03250)]
- Domain adaptation for COVID-19 用DA进行COVID-19预测
- ICONIP'22 IDPL: Intra-subdomain adaptation adversarial learning segmentation method based on Dynamic Pseudo Labels [[arxiv](https://arxiv.org/abs/2210.03435)]
- Intra-domain adaptation for segmentation 子领域对抗Adaptation
