Paper references for Transfer learning(Update weekly)

Paper list:

• "Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning". https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7404017

• "Translated learning: Transfer learning across different feature spaces" http://papers.nips.cc/paper/3492-translated-learning-transfer-learning-across-different-feature-spaces.pdf

• https://www.researchgate.net/profile/Jing_Zhang218/publication/316921517_Transfer_Learning_for_Cross-Dataset_Recognition_A_Survey/links/595e001ba6fdcc6d8562a7b6/Transfer-Learning-for-Cross-Dataset-Recognition-A-Survey.pdf a summary of transfer learning with different kind of dataset

• https://arxiv.org/pdf/1608.08614.pdf Explanation on why ImageNet is good for transfer learning.

• https://www.mdpi.com/2072-4292/9/9/907/htm transfer learning on another domain -> unsupervised learning on source task while supervised learning on target task(Radar image classification)

• https://www.sciencedirect.com/science/article/pii/S0925231216304623 related to how to transfer when training and testing dataset are not independent and identically distributed

• https://link.springer.com/chapter/10.1007/978-3-319-16199-0_2 cross-dataset analysis

• https://www.sciencedirect.com/science/article/pii/S0950705115000179 background learning of other unseen transfer learning methods

week5 added paper:

• https://arxiv.org/abs/1905.05901 - learning where and what to transfer
• https://arxiv.org/pdf/1412.3474.pdf - DDC
• https://arxiv.org/pdf/1607.01719.pdf deep CORAL loss for domain adaption
• http://ise.thss.tsinghua.edu.cn/~mlong/doc/deep-adaptation-networks-icml15.pdf DAN
• https://arxiv.org/pdf/1910.05562.pdf - DTA

week6 papers:(justify and improvement of chosen methods):

• https://arxiv.org/pdf/1808.09347.pdf - JDDA
• https://arxiv.org/abs/1602.04433 - RTN
• https://arxiv.org/abs/1911.02685 - A Comprehensive Survey on Transfer Learning
• https://arxiv.org/abs/1605.06636 - Joint Adaptation Networks
• https://openaccess.thecvf.com/content_CVPRW_2020/papers/w31/Luo_Deep_Wavelet_Network_With_Domain_Adaptation_for_Single_Image_Demoireing_CVPRW_2020_paper.pdf - AWUDN
• https://www.sciencedirect.com/science/article/pii/S003132031930425X#bib0013 - CAADA