迁移学习 材料集合
目录
1) novel_papers on transfer learning
2) novel_papers on related fileds
大部分内容 转自 GitHub:https://github.com/yuntaodu/Transfer-learning-materials
Book
《迁移学习简明手册》
https://github.com/jindongwang/transferlearning-tutorial
novel_papers
1) novel_papers on transfer learning
number | Title | Conference/journel + year | Code | Keywords | Benenit for us |
---|---|---|---|---|---|
54 | Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID (paper) | NIPS 2020 | code | contrastive learning, DA, Re-ID | contrastive learning + DA |
53 | Measuring Information Transfer in Neural Networks (paper) | arvix 2020 | maybe useful for DA | ||
52 | Open-Set Hypothesis Transfer with Semantic Consistency (paper) | arvix 2020 | source free, open set | ||
51 | Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling (paper) | ICML 2020 | stein discrepancy | a new metric that is never used in DA | |
50 | Impact of ImageNet Model Selection on Domain Adaptation(paper) | WACV 2020 workshop | shallow methods with different deep features | 实验结果很迷惑 | |
49 | Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks(paper) | arvix 2020 | pretraining | good papers | |
48 | Attract, Perturb, and Explore: Learning a Feature Alignment Network for Semi-supervised Domain Adaptation (paper) | ECCV 2020 | code | SSDA, intar-domain discrepancy | good questions |
47 | Measuring Information Transfer in Neural Networks(paper) | interesting paper | |||
46 | Neural transfer learning for natural language processing(paper) | 2019 PDH thesis | NLP, transfer lerning | very detailed related work | |
45 | When Semi-Supervised Learning Meets Transfer Learning: Training Strategies, Models and Datasets(paper) | SSL, TL, experiments | many results related to multiple SSL methods can be seen in this paper | ||
44 | Unsupervised Transfer Learning for Spatiotemporal Predictive Networks (paper) | ICML 2020 | |||
43 | Estimating Generalization under Distribution Shifts via Domain-Invariant Representations (paper) | ICML 2020 | code | new theory | recommend to read |
42 | Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation (paper) | ICML 2020 | code | ideas from theory | recommend to read |
41 | LEEP: A New Measure to Evaluate Transferability of Learned Representations (paper) | ICML 2020 | new metric for transferability | easy to use for other tasks | |
40 | Label-Noise Robust Domain Adaptation | ICML2020 | the author is a rising star | ||
39 | Progressive Graph Learning for Open-Set Domain Adaptation (paper) | ICML 2020 | code | open set DA | |
38 | Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation (paper) | ICML 2020 | code | source-free DA | recommend to read, new trneds |
37 | Graph Optimal Transport for Cross-Domain Alignment (paper) | ICML 2020 | graph for DA | connenction with GCN | |
36 | Learning Deep Kernels for Non-Parametric Two-Sample Tests (paper) | ICML 2020 | code | extend MMD to deep | |
35 | Adversarial-Learned Loss for Domain Adaptation | AAAI 2020 | noisy label, adversarial learning | ||
34 | Transfer Learning for Anomaly Detection through Localized and Unsupervised Instance Selection | AAAI 2020 | transfer learning, anamaly detection | ||
33 | Dynamic Instance Normalization for Arbitrary Style Transfer | AAAI 2020 | dynamic instance normalization | ||
32 | AdaFilter: Adaptive Filter Fine-Tuning for Deep Transfer Learning | AAAI 2020 | gated output, fine-tune | ||
31 | Bi-Directional Generation for Unsupervised Domain Adaptation | AAAI 2020 | differert feature extractor, different classifiers | connection with ICML 2019, the third term | |
30 | Discriminative Adversarial Domain Adaptation | AAAI 2020 | discriminative information with adversarial learning | ||
29 | Domain Generalization Using a Mixture of Multiple Latent Domains | AAAI 2020 | |||
28 | Multi-Source Distilling Domain Adaptation | AAAI 2020 | multi-source | ||
27 | Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision | CVPR 2020 | code | Entropy adversarial based | |
26 | Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective | CVPR 2020 | long-tailed | ||
25 | Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering | CVPR 2020 | code | cluster | |
24 | Stochastic Classifiers for Unsupervised Domain Adaptation | CVPR 2020 | stochastic two classifiers | simialer to MCD | |
23 | Progressive Adversarial Networks for Fine-Grained Domain Adaptation | CVPR 2020 | fine-grained | similar to mutil-aspect opinion analysis | |
22 | Model Adaptation: Unsupervised Domain Adaptation without Source Data | CVPR 2020 | Recommend to read, new problems | ||
21 | Towards Inheritable Models for Open-Set Domain Adaptation | CVPR 2020 | code | ||
20 | Joint Disentangling and Adaptation for Cross-Domain Person Re-Identification | ECCV 2020 | |||
19 | Extending and Analyzing Self-Supervised Learning Across Domains (paper) | ECCV 2020 | |||
18 | Dual Mixup Regularized Learning for Adversarial Domain Adaptation (paper) | ECCV 2020 | |||
17 | Label Propagation with Augmented Anchors: A Simple Semi-Supervised Learning baseline for Unsupervised Domain Adaptation (paper | ECCV 2020 | code | SSL reguralization, Anchors | new methods, good writings |
16 | Do Adversarially Robust ImageNet Models Transfer Better? | arvix 2020 | code | Many experiments | |
15 | Visualizing Transfer Learning | arvix 2020 | interesting | ||
14 | A SURVEY ON DOMAIN ADAPTATION THEORY:LEARNING BOUNDS AND THEORETICAL GUARANTEES (paper) | arvix 2020 | theory | ||
13 | SpotTune: Transfer Learning through Adaptive Fine-tuning (paper) | CVPR 2019 | code | dynamic routing is a general method | |
12 | Parameter Transfer Unit for Deep Neural Networks (paper) | PAKDD 2019 best paper | good idea, recommened to read | ||
11 | Heterogeneous Domain Adaptation via Soft Transfer Network (paper) | ACM MM 2019 | |||
10 | Information-Theoretical Learning of Discriminative Clusters for Unsupervised Domain Adaptation (paper) | ICML 2012 | |||
9 | Joint Disentangling and Adaptation for Cross-Domain Person Re-Identification (paper) | arvix 2020 | Good ideas | ||
8 | Towards Recognizing Unseen Categories in Unseen Domains (paper) | arvix 2020 | new problems | ||
7 | MiCo: Mixup Co-Training for Semi-Supervised Domain Adaptation (paper) | arvix 2020 | good framework | ||
6 | Dynamic Knowledge Distillation for Black-box Hypothesis Transfer Learning (paper | arvix 2020 | |||
5 | Simultaneous Semantic Alignment Network for Heterogeneous Domain Adaptation (paper) | ACM MM 2020 | code | ||
4 | Learning from a Complementary-label Source Domain: Theory and Algorithms(paper) | arvix 2020 | code | novel idea | |
3 | Class-Incremental Domain Adaptation(paper) | ECCV 2020 | new problems | ||
2 | Class-incremental Learning via Deep Model Consolidation (paper) | WACV 2020 | |||
1 | Adversarial Graph Representation Adaptation for Cross-Domain Facial Expression Recognition (paper) | ACM MM 2020 | similar idea with us | ||
0 | A Review of Single-Source Deep Unsupervised Visual Domain Adaptation paper | arvix 2020 | Review | a good review! It contains many results of the state-of-the-art method |
2) novel_papers on related fileds
number | Title | Conference/journel + year | Code | Keywords | Benenit for us |
---|---|---|---|---|---|
14 | Self-Loop Uncertainty: A Novel Pseudo-Label for Semi-Supervised Medical Image Segmentation (paper) | MICCAI 2020 | ssl, pseudo label | ||
13 | Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning (paper) | NIPS 2020 | semi-supervised, weight smaples | it can be used in our work | |
12 | Safe semi-supervised learning: a brief introduction (paper) | safe ssl | new concept, maybe useful for negative transfer | ||
11 | Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled Data (paper) | ICML 2020 | code | ssl, unseen class | open set, maybe useful for negative transfer |
10 | (RECORD: Resource Constrained Semi-Supervised Learning under Distribution Shifpaper) | KDD 2020 | online, distribution shift | maybe useful for negative transfer | |
9 | Adversarial Examples Improve Image Recognition (paper) | CVPR 2020 | Adversarial examples, image recognition, batch normalization | Same idea can be explored in DA | |
8 | Collaborative Graph Convolutional Networks: Unsupervised Learning Meets Semi-Supervised Learning | AAAI 2020 | unsupervised learning, semi-supervised learning | ||
7 | Self-supervised Label Augmentation via Input Transformations | ICML 2020 | code | self-supervised | ideas can be used to many tasks |
6 | Learning with Multiple Complementary Labels (paper) | ICML 2020 | |||
5 | Deep Divergence Learning (paper) | ICML 2020 | divergence | ||
4 | Confidence-Aware Learning for Deep Neural Networks (paper) | ICML 2020 | code | confidence | |
3 | Continual Learning in Human Activity Recognition:an Empirical Analysis of Regularization (paper) | ICML workshop | code | Continual learning bechmark | |
2 | Automated Phrase Mining from Massive Text Corpora (paper) | ||||
1 | Adversarially-Trained Deep Nets Transfer Better(paper | arvix 2020 | new findings | ||
0 | Learning to Combine: Knowledge Aggregation for Multi-Source Domain Adaptation | arvix (paper) | same ideas with us |
更多 DA awesome
【必看】https://github.com/jindongwang/transferlearning
https://github.com/zhaoxin94/awesome-domain-adaptation#distance-based-methods
https://github.com/barebell/DA
入门参考
本部分内容适合初学者,将一些本领域中的经典论文按照时间线进行分类、梳理,分为浅层域适应、深度域适应、对抗域适应和域适应领域四部分。
针对每一部分,列举了3-4篇经典论文,建议详读这些经典论文,泛读这些经典论文的后续论文,并对其中的部分算法进行实现。
预期学习时间为2-3个月, 详细计划安排见入门参考
围绕这些论文,曾有一个相应的讨论班,相关的日程和资料如下:
小结
Excellent Scholars
- 龙明盛 清华大学
- 庄福振 中科院计算所
- 张宇 南方科技大学
- 李汶 ETH
- 王晋东 微软亚洲研究院
- 张磊 重庆大学
- Judy Hoffman Georgia Tech
- Kate Aaenko Boston University
- Sinno Jialin Pan NTU
- Kuniaki Saito Boston University(Ph.D)
- Zhao Han CMU
- 宫博庆 Google Research
新论文追踪
科研方法论
- 督工 认知模型 链接
- 沈向洋 you are what you read 链接
- 沈向洋 how to read papers 7.18(私有), 文字版
- 王井东 how to read papers 7.21(私有, 密码同上)
- 袁路 how to read papers 7.24(私有,密码同上)
- 陈栋 how to read papers 7.27(私有,密码同上)
- 杨蛟龙 how to read papers 7.30(私有,密码同上)
- 胡瀚 how to read papers 8.2(私有,密码同上)
- 陈东东 how to read papers 8.5(私有,密码同上)
- 秦涛 do high-quality research (私有,密码同上)
Presentation
- 龙明盛 CCDM 2020 视频 , ppt
- VALSE Webinar 20-19期 迁移学习 (个人非常推荐, 对新手不友好,对进阶有帮助,质量很高!) 视频, 报告简介
- 龙明盛_NJU2019 Transfer Learning Theories and Algorithms ppt
- 龙明盛 Valse 2019 Transfer Learning_From Algorithms to Theories and Back 视频 ppt
- 游凯超 智源论坛 2019 领域适配前沿研究--场景、方法与模型选择 视频,ppt
- 王玫 2019 deep_domain_adaptation 视频, ppt
- 吴恩达 NIPS 2016 Nuts and bolts of building AI applications using Deep Learning 视频(需科学上网),ppt
来源:oschina
链接:https://my.oschina.net/u/4347922/blog/4772171