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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. We use a probabilistic contrastiveloss which induces the latent space to capture information that is maximally usefulto predict future samples. It also makes the model tractable by using negativesampling.
learning: modeling high-level representations from raw observations remains elusive. Further, it is not always clear what the ideal representation is and if it is possible that one can learn such a representation without additional supervision or specialization to a particular data modality.
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