Towards Better Understanding of Disentangled Representations via Mutual Information

25 Nov 2019Xiaojiang YangWendong BiYitong SunYu ChengJunchi Yan

Most existing works on disentangled representation learning are solely built upon an marginal independence assumption: all factors in disentangled representations should be statistically independent. This assumption is necessary but definitely not sufficient for the disentangled representations without additional inductive biases in the modeling process, which is shown theoretically in recent studies... (read more)

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