迁移学习 材料集合

迁移学习 材料集合

目录

迁移学习 材料集合

Book

novel_papers

1) novel_papers on transfer learning

2) novel_papers on related fileds

更多 DA awesome​​​​​​​

入门参考

小结

Excellent Scholars

新论文追踪

科研方法论

Presentation

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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：

• week 1
• week 2
• week1 & week2 reference
• week 3
• week 4
• week 5

小结

• 迁移学习理论 ppt, pdf
• Online Transfer Learning pdf

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

• Topic: domain adaptation
• Topic: transfer learning
• Topic: Semi-supervised

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