Implicit Bias of Large Depth Networks: a Notion of Rank for Nonlinear Functions

Arthur Jacot

Keywords: Deep Neural Networks, implicit bias, representation cost, sparsity

TL;DR: The representation cost of DNNs converges to a notion of nonlinear rank as the depth grows to infinity. This bias towards low-rank functions extends to large but finite widths.

Abstract: We show that the representation cost of fully connected neural networks with homogeneous nonlinearities - which describes the implicit bias in function space of networks with $L_2$-regularization or with losses such as the cross-entropy - converges as the depth of the network goes to infinity to a notion of rank over nonlinear functions. We then inquire under which conditions the global minima of the loss recover the `true' rank of the data: we show that for too large depths the global minimum will be approximately rank 1 (underestimating the rank); we then argue that there is a range of depths which grows with the number of datapoints where the true rank is recovered. Finally, we discuss the effect of the rank of a classifier on the topology of the resulting class boundaries and show that autoencoders with optimal nonlinear rank are naturally denoising.

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