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- W3178103610 abstract "We show that a popular self-supervised learning method, InfoNCE, is a special case of a new family of unsupervised learning methods, the self-supervised variational autoencoder (SSVAE). SSVAEs circumvent the usual VAE requirement to reconstruct the data by using a carefully chosen implicit decoder. The InfoNCE objective was motivated as a simplified parametric mutual information estimator. Under one choice of prior, the SSVAE objective (i.e. the ELBO) is exactly equal to the mutual information (up to constants). Under an alternative choice of prior, the SSVAE objective is exactly equal to the simplified parametric mutual information estimator used in InfoNCE (up to constants). Importantly, the use of simplified parametric mutual information estimators is believed to be critical to obtain good high-level representations, and the SSVAE framework naturally provides a principled justification for using prior information to choose these estimators." @default.
- W3178103610 created "2021-07-19" @default.
- W3178103610 creator A5062539506 @default.
- W3178103610 date "2021-07-06" @default.
- W3178103610 modified "2023-09-27" @default.
- W3178103610 title "InfoNCE is a variational autoencoder." @default.
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