Controllable Invariance through Adversarial Feature Learning

NeurIPS 2017 Qizhe XieZihang DaiYulun DuEduard HovyGraham Neubig

Learning meaningful representations that maintain the content necessary for a particular task while filtering away detrimental variations is a problem of great interest in machine learning. In this paper, we tackle the problem of learning representations invariant to a specific factor or trait of data... (read more)

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