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Abstract
Representation Learning withContrastive Predictive CodingAaron van den supervised learning has enabled great progress in many applications, unsu-pervised learning has not seen such widespread adoption, and remains an importantand challenging endeavor for artificial intelligence. In this work, we propose auniversal unsupervised learning approach to extract useful representations fromhigh-dimensional data, which we call Contrastive Predictive Coding. The key in-sight of our model is to learn such representations by predicting the future inlatentspace by using powerful autoregressive models.
Aaron van den Oord DeepMind avdnoord@google.com Yazhe Li DeepMind yazhe@google.com Oriol Vinyals DeepMind vinyals@google.com Abstract While supervised learning has enabled great progress in many applications, unsu-pervised learning has not seen such widespread adoption, and remains an important and challenging endeavor for artificial intelligence.
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