Unsupervised Representation Learning by Sorting Sequences

ICCV, pp. 667-676, 2017.

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We present an unsupervised representation method through solving the sequence sorting problem

Abstract:

We present an unsupervised representation learning approach using videos without semantic labels. We leverage the temporal coherence as a supervisory signal by formulating representation learning as a sequence sorting task. We take temporally shuffled frames (i.e., in non-chronological order) as inputs and train a convolutional neural net...More

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