Spontaneous Emergence of Robustness to Light Variation in CNNs With a Precortically Inspired Module

IF 2.7 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Computation Pub Date : 2024-08-19 DOI:10.1162/neco_a_01691

J. Petkovic;R. Fioresi

引用次数: 0

Abstract

The analogies between the mammalian primary visual cortex and the structure of CNNs used for image classification tasks suggest that the introduction of an additional preliminary convolutional module inspired by the mathematical modeling of the precortical neuronal circuits can improve robustness with respect to global light intensity and contrast variations in the input images. We validate this hypothesis using the popular databases MNIST, FashionMNIST, and SVHN for these variations once an extra module is added.

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带有前皮质启发模块的 CNN 对光线变化的自发鲁棒性。

哺乳动物初级视觉皮层与用于图像分类任务的 CNN 结构之间的类比表明，在皮层前神经元电路数学建模的启发下，引入额外的初步卷积模块可以提高输入图像中全局光强度和对比度变化的鲁棒性。我们使用流行的数据库 MNIST、FashionMNIST 和 SVHN 验证了这一假设，即一旦添加了额外的模块，这些变化就会出现。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Neural Computation 工程技术-计算机：人工智能

CiteScore

6.30

自引率

3.40%

发文量

审稿时长

3.0 months

期刊介绍： Neural Computation is uniquely positioned at the crossroads between neuroscience and TMCS and welcomes the submission of original papers from all areas of TMCS, including: Advanced experimental design; Analysis of chemical sensor data; Connectomic reconstructions; Analysis of multielectrode and optical recordings; Genetic data for cell identity; Analysis of behavioral data; Multiscale models; Analysis of molecular mechanisms; Neuroinformatics; Analysis of brain imaging data; Neuromorphic engineering; Principles of neural coding, computation, circuit dynamics, and plasticity; Theories of brain function.