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