Unsupervised Representation Learning by Sorting Sequences

arxiv.org

A fascinating paper presenting an unsupervised representation learning approach using videos without semantic labels. The authors leverage the temporal coherence as a supervisory signal by formulating representation learning as a sequence sorting task. They take temporally shuffled frames (i.e., in non-chronological order) as inputs and train a convolutional neural network to sort the shuffled sequences.

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