Domain Adaptation By Backpropagation

Ty cpaper ti unsupervised domain adaptation by backpropagation au yaroslav ganin au victor lempitsky bt proceedings of the 32nd international conference on machine learning py 2015 06 01 da 2015 06 01 ed francis bach ed david blei id pmlr v37 ganin15 pb pmlr sp 1180 dp. In this paper we propose a method for an open set domain adaptation scenario which utilizes adversarial training. The proposed architecture includes a deep feature extractor green and a deep label predictor blue which together form a standard feed forward architecture.

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However it doesn t help in situations where there is very little or no overlap between the training and testing distributions.

Domain adaptation by backpropagation. Unsupervised domain adaptation by backpropagation. Yaroslav ganin victor lempitsky topic. Open set domain adaptation by backpropagation kuniaki saito1 shohei yamamoto1 yoshitaka ushiku1 and tatsuya harada1 2 1the university of tokyo 2riken fk saito yamamoto ushiku haradag mi t u tokyo ac jp abstract.

Unsupervised domain adaptation by backpropagation authors. About this paper title. Domain adaptation hmdbfull to ucf revgrad accuracy 74 44 4 domain adaptation.

However most existing distribution matching methods for domain adaptation do not work well in this setting because unknown target samples should not be aligned with the source. Unsupervised domain adaptation by backpropagation阅读笔记. Unsupervised domain adaptation by backpropagation.

Domain adaptation from icml 2015. Numerous algorithms have been proposed for transferring. Unsupervised domain adaptation by backpropagation figure 1. Unsupervised domain adaptation is achieved by adding a domain classiﬁer red connected to the.

In the absence of labeled data for a certain task domain adaptation often provides an attractive option given that labeled data of similar nature but from a different domain e g. Domain adaption by backpropagation the covariate shift by importance weighting method above is reasonably effective when there is a slight shift in distributions between the training and test data.

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