{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/representation-learning-with-contrastive","title":"Representation Learning with Contrastive Predictive Coding","arxiv_id":"1807.03748","date":"2018-07-10","proceeding":null,"authors":["Aaron van den Oord","Yazhe Li","Oriol Vinyals"],"abstract":"While supervised learning has enabled great progress in many applications,\nunsupervised learning has not seen such widespread adoption, and remains an\nimportant and challenging endeavor for artificial intelligence. In this work,\nwe propose a universal unsupervised learning approach to extract useful\nrepresentations from high-dimensional data, which we call Contrastive\nPredictive Coding. The key insight of our model is to learn such\nrepresentations by predicting the future in latent space by using powerful\nautoregressive models. We use a probabilistic contrastive loss which induces\nthe latent space to capture information that is maximally useful to predict\nfuture samples. 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