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arXiv · 2207.05532

Utilizing Excess Resources in Training Neural Networks

Amit Henig·Raja Giryes
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Abstract

In this work, we suggest Kernel Filtering Linear Overparameterization (KFLO), where a linear cascade of filtering layers is used during training to improve network performance in test time. We implement this cascade in a kernel filtering fashion, which prevents the trained architecture from becoming unnecessarily deeper. This also allows using our approach with almost any network architecture and let combining the filtering layers into a single layer in test time. Thus, our approach does not add computational complexity during inference. We demonstrate the advantage of KFLO on various network models and datasets in supervised learning.

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Amit Henig, Raja Giryes. 2022-07-12. Utilizing Excess Resources in Training Neural Networks. https://arxiv.org/abs/2207.05532

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