A Simple Stochastic Regularization Technique for Avoiding Overfitting in Low Resource Image Classification
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Date
2023
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Drop type technique, as a method that can effectively regulate the co-adaptations and prediction ability of neural network units, is widely used in model parameter optimization to reduce overfitting problems. However, low resource image classification faces serious overfitting problems, and the data sparsity problem weakens or even disappears the effectiveness of most regularization methods. This paper is inspired by the value iteration strategy and attempts a Drop type method based on Metcalfe's law, named Metcalfe-Drop. The experimental results indicate that using Metcalfe-Drop technique as a basis to determine parameter sharing is more effective than randomly controlling neurons according to a certain probability. Our code is available at https://gitee.com/giteetu/metcalfe-drop.git.
Description
CCS Concepts: Computing methodologies -> Image processing; Neural networks; Image representations
@inproceedings{10.2312:pg.20231281,
booktitle = {Pacific Graphics Short Papers and Posters},
editor = {Chaine, Raphaëlle and Deng, Zhigang and Kim, Min H.},
title = {{A Simple Stochastic Regularization Technique for Avoiding Overfitting in Low Resource Image Classification}},
author = {Ji, Ya Tu and Wang, Bai Lun and Dai, Ling Jie and Yao, Miao Miao and Li, Xiao Mei and Ren, Qing Dao Er Ji and Shi, Bao and Wu, Nier E. and Lu, Min and Liu, Na and Zhuang, Xu Fei and Xu, Xuan Xuan and Wang, Li},
year = {2023},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-234-9},
DOI = {10.2312/pg.20231281}
}