-WACV'23 Cross-Domain Video Anomaly Detection without Target Domain Adaptation [[arxiv](https://arxiv.org/abs/2212.07010)]
-Cross-domain video anomaly detection without target domain adaptation 跨域视频异常检测
-TPAMI'23 Source-Free Unsupervised Domain Adaptation: A Survey [[arxiv](http://arxiv.org/abs/2301.00265)]
-A survey on source-free domain adaptation 关于source-free DA的一个最新综述
- Co-Learning with Pre-Trained Networks Improves Source-Free Domain Adaptation [[arxiv](https://arxiv.org/abs/2212.07585)]
- Pre-trained models for source-free domain adaptation 用预训练模型进行source-free DA
*Updated at 2022-12-07:*
- TMLR'22 A Unified Survey on Anomaly, Novelty, Open-Set, and Out of-Distribution Detection: Solutions and Future Challenges [[openreview](https://openreview.net/forum?id=aRtjVZvbpK)]
- A recent survey on OOD/anomaly detection 一篇最新的关于OOD/anomaly detection的综述
- NeurIPS'18 A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks [[paper](https://proceedings.neurips.cc/paper/2018/hash/abdeb6f575ac5c6676b747bca8d09cc2-Abstract.html)]
- Using class-conditional distribution for OOD detection 使用类条件概率进行OOD检测
- Embed discrete representation for OOD generalization 在ViT中加入离散表征增强OOD性能
*Updated at 2022-12-02:*
- CONDA: Continual Unsupervised Domain Adaptation Learning in Visual Perception for Self-Driving Cars [[arxiv](https://arxiv.org/abs/2212.00621)]
- Continual DA for self-driving cars 连续的domain adaptation用于自动驾驶
- Finetune like you pretrain: Improved finetuning of zero-shot vision models [[arxiv]](http://arxiv.org/abs/2212.00638)]
- Improved fine-tuning of zero-shot models 针对zero-shot model提高fine-tuneing
*Updated at 2022-11-25:*
- Robust Mean Teacher for Continual and Gradual Test-Time Adaptation [[arxiv](https://arxiv.org/abs/2211.13081)]
- Mean teacher for test-time adaptation 在测试时用mean teacher进行适配
- Learning to Learn Domain-invariant Parameters for Domain Generalization [[arxiv](Learning to Learn Domain-invariant Parameters for Domain Generalization)]
- Learning to learn domain-invariant parameters for DG 元学习进行domain generalization
- HMOE: Hypernetwork-based Mixture of Experts for Domain Generalization [[arxiv](https://arxiv.org/abs/2211.08253)]
- Hypernetwork-based ensembling for domain generalization 超网络构成的集成学习用于domain generalization
- The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning [[arxiv](https://arxiv.org/abs/2106.15831)]
- OOD using fine-tuning 系统总结了基于fine-tuning进行OOD的一些结果
*Updated at 2022-11-21:*
- GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspective [[arxiv](https://arxiv.org/abs/2211.08073)]
- OOD for natural language processing evaluation 提出GLUE-X用于OOD在NLP数据上的评估
- CVPR'22 Delving Deep Into the Generalization of Vision Transformers Under Distribution Shifts [[arxiv](https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Delving_Deep_Into_the_Generalization_of_Vision_Transformers_Under_Distribution_CVPR_2022_paper.html)]
- Vision transformers generalization under distribution shifts 评估ViT的分布漂移
- NeurIPS'22 Models Out of Line: A Fourier Lens on Distribution Shift Robustness [[arxiv](https://openreview.net/forum?id=YZ-N-sejjwO)]
- A fourier lens on distribution shift robustness 通过傅里叶视角来看分布漂移的鲁棒性
- CVPR'22 Does Robustness on ImageNet Transfer to Downstream Tasks? [[arxiv](https://openaccess.thecvf.com/content/CVPR2022/papers/Yamada_Does_Robustness_on_ImageNet_Transfer_to_Downstream_Tasks_CVPR_2022_paper.pdf)]
- Does robustness on imagenet transfer lto downstream tasks?
*Updated at 2022-11-14:*
- Normalization Perturbation: A Simple Domain Generalization Method for Real-World Domain Shifts [[arxiv](https://arxiv.org/abs/2211.04393)]
- Normalization perturbation for domain generalization 通过归一化扰动来进行domain generalization
- FIXED: Frustraitingly easy domain generalization using Mixup [[arxiv](https://arxiv.org/pdf/2211.05228.pdf)]
- 使用Mixup进行domain generalization
- Learning to Learn Domain-invariant Parameters for Domain Generalization [[arxiv](https://arxiv.org/abs/2211.04582)]
- Learning to learn domain-invariant parameters for domain generalization
*Updated at 2022-11-07:*
- NeurIPS'22 Improved Fine-Tuning by Better Leveraging Pre-Training Data [[openreview](https://openreview.net/forum?id=YTXIIc7cAQ)]
- Using pre-training data for fine-tuning 用预训练数据来做微调
- NeurIPS'22 Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning [[openreview](https://openreview.net/forum?id=NjImFaBEHl)]
- Adaptive contrastive learning for source-free DA 自适应的对比学习用于source-free DA
- NeurIPS'22 LOG: Active Model Adaptation for Label-Efficient OOD Generalization [[openreview](https://openreview.net/forum?id=VdQWVdT_8v)]
- Model adaptation for label-efficient OOD generalization
- NeurIPS'22 MetaTeacher: Coordinating Multi-Model Domain Adaptation for Medical Image Classification [[openreview](https://openreview.net/forum?id=AQd4ugzALQ1)]
- Multi-model domain adaptation mor medical image classification 多模型DA用于医疗数据
- NeurIPS'22 Domain Adaptation under Open Set Label Shift [[openreview](https://openreview.net/forum?id=OMZG4vsKmm7)]
- Domain adaptation under open set label shift 在开放集的label shift中的DA
*Updated at 2022-11-03:*
- 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 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
- 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
- NeurIPS'22 Your Out-of-Distribution Detection Method is Not Robust! [[openreview](https://openreview.net/forum?id=YUEP3ZmkL1)]
@@ -238,6 +143,7 @@ Here are some articles on transfer learning theory and survey.
**Survey (综述文章)：**
- 2023 Source-Free Unsupervised Domain Adaptation: A Survey [[arxiv](http://arxiv.org/abs/2301.00265)]
- 2022 [Transfer Learning for Future Wireless Networks: A Comprehensive Survey](https://arxiv.org/abs/2102.07572)
- 2022 [A Review of Deep Transfer Learning and Recent Advancements](https://arxiv.org/abs/2201.09679)
- 2022 [Transferability in Deep Learning: A Survey](https://paperswithcode.com/paper/transferability-in-deep-learning-a-survey), from Mingsheng Long in THU.
- WACV'23 Cross-Domain Video Anomaly Detection without Target Domain Adaptation [[arxiv](https://arxiv.org/abs/2212.07010)]
- Cross-domain video anomaly detection without target domain adaptation 跨域视频异常检测
...
...
@@ -2535,4 +2541,4 @@ Here, we list some papers by topic. For list by date, please refer to [papers by
## Transfer learning applications
See [HERE](https://github.com/jindongwang/transferlearning/blob/master/doc/transfer_learning_application.md) for a full list of transfer learning applications.
See [HERE](https://github.com/jindongwang/transferlearning/blob/master/doc/transfer_learning_application.md) for a full list of transfer learning applications.
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)
-[2022-12](#2022-12)
-[2022-11](#2022-11)
-[2022-10](#2022-10)
-[2022-09](#2022-09)
...
...
@@ -21,11 +22,100 @@ Here, we list some papers related to transfer learning by date (starting from 20
-[2021-08](#2021-08)
-[2021-07](#2021-07)
## 2022-12
- WACV'23 Cross-Domain Video Anomaly Detection without Target Domain Adaptation [[arxiv](https://arxiv.org/abs/2212.07010)]
- Cross-domain video anomaly detection without target domain adaptation 跨域视频异常检测
- Co-Learning with Pre-Trained Networks Improves Source-Free Domain Adaptation [[arxiv](https://arxiv.org/abs/2212.07585)]
- Pre-trained models for source-free domain adaptation 用预训练模型进行source-free DA
- TMLR'22 A Unified Survey on Anomaly, Novelty, Open-Set, and Out of-Distribution Detection: Solutions and Future Challenges [[openreview](https://openreview.net/forum?id=aRtjVZvbpK)]
- A recent survey on OOD/anomaly detection 一篇最新的关于OOD/anomaly detection的综述
- NeurIPS'18 A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks [[paper](https://proceedings.neurips.cc/paper/2018/hash/abdeb6f575ac5c6676b747bca8d09cc2-Abstract.html)]
- Using class-conditional distribution for OOD detection 使用类条件概率进行OOD检测
- Domain adaptation in semantic segmentation 语义分割域适应
- Robust Mean Teacher for Continual and Gradual Test-Time Adaptation [[arxiv](https://arxiv.org/abs/2211.13081)]
- Mean teacher for test-time adaptation 在测试时用mean teacher进行适配
- Learning to Learn Domain-invariant Parameters for Domain Generalization [[arxiv](Learning to Learn Domain-invariant Parameters for Domain Generalization)]
- Learning to learn domain-invariant parameters for DG 元学习进行domain generalization
- HMOE: Hypernetwork-based Mixture of Experts for Domain Generalization [[arxiv](https://arxiv.org/abs/2211.08253)]
- Hypernetwork-based ensembling for domain generalization 超网络构成的集成学习用于domain generalization
- The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning [[arxiv](https://arxiv.org/abs/2106.15831)]
- OOD using fine-tuning 系统总结了基于fine-tuning进行OOD的一些结果
- GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspective [[arxiv](https://arxiv.org/abs/2211.08073)]
- OOD for natural language processing evaluation 提出GLUE-X用于OOD在NLP数据上的评估
- CVPR'22 Delving Deep Into the Generalization of Vision Transformers Under Distribution Shifts [[arxiv](https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Delving_Deep_Into_the_Generalization_of_Vision_Transformers_Under_Distribution_CVPR_2022_paper.html)]
- Vision transformers generalization under distribution shifts 评估ViT的分布漂移
- NeurIPS'22 Models Out of Line: A Fourier Lens on Distribution Shift Robustness [[arxiv](https://openreview.net/forum?id=YZ-N-sejjwO)]
- A fourier lens on distribution shift robustness 通过傅里叶视角来看分布漂移的鲁棒性
- CVPR'22 Does Robustness on ImageNet Transfer to Downstream Tasks? [[arxiv](https://openaccess.thecvf.com/content/CVPR2022/papers/Yamada_Does_Robustness_on_ImageNet_Transfer_to_Downstream_Tasks_CVPR_2022_paper.pdf)]
- Does robustness on imagenet transfer lto downstream tasks?
- Normalization Perturbation: A Simple Domain Generalization Method for Real-World Domain Shifts [[arxiv](https://arxiv.org/abs/2211.04393)]
- Normalization perturbation for domain generalization 通过归一化扰动来进行domain generalization
- FIXED: Frustraitingly easy domain generalization using Mixup [[arxiv](https://arxiv.org/pdf/2211.05228.pdf)]
- 使用Mixup进行domain generalization
- Learning to Learn Domain-invariant Parameters for Domain Generalization [[arxiv](https://arxiv.org/abs/2211.04582)]
- Learning to learn domain-invariant parameters for domain generalization
- NeurIPS'22 Improved Fine-Tuning by Better Leveraging Pre-Training Data [[openreview](https://openreview.net/forum?id=YTXIIc7cAQ)]
- Using pre-training data for fine-tuning 用预训练数据来做微调
- NeurIPS'22 Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning [[openreview](https://openreview.net/forum?id=NjImFaBEHl)]
- Adaptive contrastive learning for source-free DA 自适应的对比学习用于source-free DA
- NeurIPS'22 LOG: Active Model Adaptation for Label-Efficient OOD Generalization [[openreview](https://openreview.net/forum?id=VdQWVdT_8v)]
- Model adaptation for label-efficient OOD generalization
- NeurIPS'22 MetaTeacher: Coordinating Multi-Model Domain Adaptation for Medical Image Classification [[openreview](https://openreview.net/forum?id=AQd4ugzALQ1)]
- Multi-model domain adaptation mor medical image classification 多模型DA用于医疗数据
- NeurIPS'22 Domain Adaptation under Open Set Label Shift [[openreview](https://openreview.net/forum?id=OMZG4vsKmm7)]
- Domain adaptation under open set label shift 在开放集的label shift中的DA
- 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 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
- 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
- NeurIPS'22 Your Out-of-Distribution Detection Method is Not Robust! [[openreview](https://openreview.net/forum?id=YUEP3ZmkL1)]
