Fetching the paper…
Reading the bibliography…
Studies show that the representations learned by deep neural networks can be transferred to similar prediction tasks in other domains for which we do not have enough labeled data.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
Earlier work this paper cites.
On causal and anticausal learning
Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, and Joris Mooij · 2012
Earlier work this paper cites.
Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation
Masashi Sugiyama and Motoaki Kawanabe · 2012
Earlier work this paper cites.
A kernel two-sample test
A. Gretton, K. Borgwardt, M. Rasch, B. Schölkopf, and A. Smola · 2012
Earlier work this paper cites.
Decaf: A deep convolutional activation feature for generic visual recognition
Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell · 2014
Earlier work this paper cites.
How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
Earlier work this paper cites.
Deep domain confusion: Maximizing for domain invariance
Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, and Trevor Darrell · 2014
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael S. Bernstein, Alexander C. Berg, and Fei-Fei Li · 2014
Earlier work this paper cites.
Simultaneous deep transfer across domains and tasks
Eric Tzeng, Judy Hoffman, Trevor Darrell, and Kate Saenko · 2015
Earlier work this paper cites.
Learning transferable features with deep adaptation networks
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael I Jordan · 2015
Earlier work this paper cites.
Return of frustratingly easy domain adaptation
Baochen Sun, Jiashi Feng, and Kate Saenko · 2015
Cited alongside, same era.
Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2015
Cited alongside, same era.
Visual causal feature learning
Krzysztof Chalupka, Pietro Perona, and Frederick Eberhardt · 2015
Cited alongside, same era.
Binaryconnect: Training deep neural networks with binary weights during propagations
Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David · 2015
Cited alongside, same era.
Unsupervised domain adaptation with residual transfer networks
Mingsheng Long, Jianmin Wang, and Michael I. Jordan · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Later among the works it cites.
Supervised representation learning with double encoding-layer autoencoder for transfer learning
Fuzhen Zhuang, Xiaohu Cheng, Ping Luo, Sinno Jialin Pan, and Qing He · 2017
Later among the works it cites.
Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
Later among the works it cites.
Human attention in visual question answering: Do humans and deep networks look at the same regions?
Abhishek Das, Harsh Agrawal, Larry Zitnick, Devi Parikh, and Dhruv Batra · 2017
Later among the works it cites.
Deep visual domain adaptation: A survey
Mei Wang and Weihong Deng · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Fine-to-coarse knowledge transfer for low-res image classification
Xingchao Peng, Judy Hoffman, Stella X. Yu, and Kate Saenko · 2016
Cited alongside, same era.
Deep transfer learning with joint adaptation networks
Mingsheng Long, Jianmin Wang, and Michael I. Jordan · 2016
Cited alongside, same era.
Deep CORAL: correlation alignment for deep domain adaptation
Baochen Sun and Kate Saenko · 2016
Cited alongside, same era.
Coupled generative adversarial networks
Ming-Yu Liu and Oncel Tuzel · 2016
Cited alongside, same era.
Causal inference using invariant prediction: identification and confidence intervals
J. Peters, P. Bühlmann, and N. Meinshausen · 2016
Cited alongside, same era.
Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik
Cited in the paper.
Invariant models for causal transfer learning
Mateo Rojas-Carulla, Bernhard Schölkopf, Richard Turner, and Jonas Peters · 2018
Later among the works it cites.
Stable prediction across unknown environments
Kun Kuang, Ruoxuan Xiong, Peng Cui, Susan Athey, and Bo Li · 2018
Later among the works it cites.
Multimodal explanations: Justifying decisions and pointing to the evidence
Dong Huk Park, Lisa Anne Hendricks, Zeynep Akata, Anna Rohrbach, Bernt Schiele, Trevor Darrell, and Marcus Rohrbach · 2018
Later among the works it cites.
Unsupervised domain adaptation: An adaptive feature norm approach
Ruijia Xu, Guanbin Li, Jihan Yang, and Liang Lin · 2018
Later among the works it cites.