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• W3176131920 abstract "Deep equilibrium networks (DEQs) are a new class of models that eschews traditional depth in favor of finding the fixed point of a single nonlinear layer. These models have been shown to achieve performance competitive with the state-of-the-art deep networks while using significantly less memory. Yet they are also slower, brittle to architectural choices, and introduce potential instability to the model. In this paper, we propose a regularization scheme for DEQ models that explicitly regularizes the Jacobian of the fixed-point update equations to stabilize the learning of equilibrium models. We show that this regularization adds only minimal computational cost, significantly stabilizes the fixed-point convergence in both forward and backward passes, and scales well to high-dimensional, realistic domains (e.g., WikiText-103 language modeling and ImageNet classification). Using this method, we demonstrate, for the first time, an implicit-depth model that runs with approximately the same speed and level of performance as popular conventional deep networks such as ResNet-101, while still maintaining the constant memory footprint and architectural simplicity of DEQs. Code is available at https://github.com/locuslab/deq ." @default.
• W3176131920 created "2021-07-05" @default.
• W3176131920 creator A5006181255 @default.
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• W3176131920 date "2021-06-27" @default.
• W3176131920 modified "2023-10-16" @default.
• W3176131920 title "Stabilizing Equilibrium Models by Jacobian Regularization" @default.
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• W3176131920 doi "https://doi.org/10.48550/arxiv.2106.14342" @default.
• W3176131920 hasPublicationYear "2021" @default.
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