Computer Science > Machine Learning
arXiv:2207.05532 (cs)
[Submitted on 12 Jul 2022]
Title:Utilizing Excess Resources in Training Neural Networks
View a PDF of the paper titled Utilizing Excess Resources in Training Neural Networks, by Amit Henig and Raja Giryes
View PDFAbstract: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.
| Comments: | Accepted to ICIP 2022. Code available atthis https URL |
| Subjects: | Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2207.05532 [cs.LG] |
| (orarXiv:2207.05532v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2207.05532 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Utilizing Excess Resources in Training Neural Networks, by Amit Henig and Raja Giryes
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