Utilizing Excess Resources in Training Neural Networks

• URL: http://arxiv.org/abs/2207.05532v1
• Date: Tue, 12 Jul 2022 13:48:40 GMT
• Title: Utilizing Excess Resources in Training Neural Networks
• Authors: Amit Henig and Raja Giryes
• Abstract summary: We implement a linear cascade of filtering layers 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. We demonstrate the advantage of KFLO on various network models and datasets in supervised learning.
• Score: 41.07083436560303
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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