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The method trains a network using\ntemporal cycle consistency (TCC), a differentiable cycle-consistency loss that\ncan be used to find correspondences across time in multiple videos. The\nresulting per-frame embeddings can be used to align videos by simply matching\nframes using the nearest-neighbors in the learned embedding space.\n  To evaluate the power of the embeddings, we densely label the Pouring and\nPenn Action video datasets for action phases. We show that (i) the learned\nembeddings enable few-shot classification of these action phases, significantly\nreducing the supervised training requirements; and (ii) TCC is complementary to\nother methods of self-supervised learning in videos, such as Shuffle and Learn\nand Time-Contrastive Networks. The embeddings are also used for a number of\napplications based on alignment (dense temporal correspondence) between video\npairs, including transfer of metadata of synchronized modalities between videos\n(sounds, temporal semantic labels), synchronized playback of multiple videos,\nand anomaly detection. 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