Resources about Transfer Learning

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Table of contents

• Latest
• Introduction to transfer learning
• Survey papers for transfer learning
• Matlab and Python Available codes
• Scholars
• Domain adaptation articles
• Popular methods and my explanations
• Datasets
• Popular methods
• Papers

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0.Latest

• Learning To Transfer，
• Deep Transfer Learning with Joint Adaptation Networks， code

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1.Introduction

https://www.youtube.com/watch?v=qD6iD4TFsdQ

• Transitive transfer learning
• Distant Domain Transfer Learning
• A survey on transfer learning

2.Survey

• A survey on transfer learning
• Cross-dataset recognition: a survey
• A survey on multi-task learning
• A survey of transfer learning
• Transfer Learning and Reinforcement Learning。
• A survey of multi-domain transfer learning
• Computer Vision domain adaptation survey。
• Theoretical Analysis

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3.Codes

Here

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4.Scholars

• Qiang Yang：IEEE/AAAI/IAPR/AAAS fellow。[Google scholar]
• Sinno Jialin Pan：[Google scholar]
• Wenyuan Dai：
• Lixin Duan
• Fuzhen Zhuang：[Google scholar]
• Mingsheng Long：[Google scholar]

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5.Domain adaptation

Domain adaptation

6.Popular methods

• Transfer component analysis, TCA

  • Domain adaptation via tranfer component analysis

• joint distribution adaptation，JDA

  • Transfer Feature Learning with Joint Distribution Adaptation

• Geodesic flow kernel, GFK

  • Geodesic flow kernel for unsupervised domain adaptation

• Transfer Kernel Learning, TKL

  • Domain invariant transfer kernel learning

• Deep Adaptation Network, DAN

  • [learning transferable features with deep adaptation networks]

• Joint Adaptation Network, JAN

  • Deep Transfer Learning with Joint Adaptation Networks

7.Papers：

• Distilling the knowledge in a neural network (2015), G. Hinton et al. [pdf]
• Deep neural networks are easily fooled: High confidence predictions for unrecognizable images (2015), A. Nguyen et al. [pdf]
• How transferable are features in deep neural networks? (2014), J. Yosinski et al. [pdf]
• CNN features off-the-Shelf: An astounding baseline for recognition (2014), A. Razavian et al. [pdf]
• Learning and transferring mid-Level image representations using convolutional neural networks (2014), M. Oquab et al. [pdf]
• Visualizing and understanding convolutional networks (2014), M. Zeiler and R. Fergus [pdf]
• Decaf: A deep convolutional activation feature for generic visual recognition (2014), J. Donahue et al. [pdf]