Data-driven Regularization via Racecar Training for Generalizing Neural Networks

• URL: http://arxiv.org/abs/2007.00024v1
• Date: Tue, 30 Jun 2020 18:00:41 GMT
• Title: Data-driven Regularization via Racecar Training for Generalizing Neural Networks
• Authors: You Xie, Nils Thuerey
• Abstract summary: We propose a novel training approach for improving the generalization in neural networks. We show how our formulation is easy to realize in practical network architectures via a reverse pass. Networks trained with our approach show more balanced mutual information between input and output throughout all layers, yield improved explainability and, exhibit improved performance for a variety of tasks and task transfers.
• Score: 28.08782668165276
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a novel training approach for improving the generalization in neural networks. We show that in contrast to regular constraints for orthogonality, our approach represents a {\em data-dependent} orthogonality constraint, and is closely related to singular value decompositions of the weight matrices. We also show how our formulation is easy to realize in practical network architectures via a reverse pass, which aims for reconstructing the full sequence of internal states of the network. Despite being a surprisingly simple change, we demonstrate that this forward-backward training approach, which we refer to as {\em racecar} training, leads to significantly more generic features being extracted from a given data set. Networks trained with our approach show more balanced mutual information between input and output throughout all layers, yield improved explainability and, exhibit improved performance for a variety of tasks and task transfers.

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