Analysis of feature learning in weight-tied autoencoders via the mean field lens

• URL: http://arxiv.org/abs/2102.08373v1
• Date: Tue, 16 Feb 2021 18:58:37 GMT
• Title: Analysis of feature learning in weight-tied autoencoders via the mean field lens
• Authors: Phan-Minh Nguyen
• Abstract summary: We analyze a class of two-layer weight-tied nonlinear autoencoders in the mean field framework. Models trained with gradient descent are shown to admit a mean field limiting dynamics. Experiments on real-life data demonstrate an interesting match with the theory.
• Score: 3.553493344868413
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Autoencoders are among the earliest introduced nonlinear models for unsupervised learning. Although they are widely adopted beyond research, it has been a longstanding open problem to understand mathematically the feature extraction mechanism that trained nonlinear autoencoders provide. In this work, we make progress in this problem by analyzing a class of two-layer weight-tied nonlinear autoencoders in the mean field framework. Upon a suitable scaling, in the regime of a large number of neurons, the models trained with stochastic gradient descent are shown to admit a mean field limiting dynamics. This limiting description reveals an asymptotically precise picture of feature learning by these models: their training dynamics exhibit different phases that correspond to the learning of different principal subspaces of the data, with varying degrees of nonlinear shrinkage dependent on the $\ell_{2}$-regularization and stopping time. While we prove these results under an idealized assumption of (correlated) Gaussian data, experiments on real-life data demonstrate an interesting match with the theory. The autoencoder setup of interests poses a nontrivial mathematical challenge to proving these results. In this setup, the "Lipschitz" constants of the models grow with the data dimension $d$. Consequently an adaptation of previous analyses requires a number of neurons $N$ that is at least exponential in $d$. Our main technical contribution is a new argument which proves that the required $N$ is only polynomial in $d$. We conjecture that $N\gg d$ is sufficient and that $N$ is necessarily larger than a data-dependent intrinsic dimension, a behavior that is fundamentally different from previously studied setups.

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