[en] Convolutional layers, one of the basic building blocks of deep learning architectures, contain numerous trainable filters for feature extraction. These filters operate independently which can result in distinct filters learning similar weights and extracting similar features. In contrast, competition mechanisms in the brain contribute to the sharpening of the responses of activated neurons, enhancing the contrast and selectivity of individual neurons towards specific stimuli, and simultaneously increasing the diversity of responses across the population of neurons. Inspired by this observation, this paper proposes a novel convolutional layer based on the theory of predictive coding, in which each filter effectively tries to block other filters from responding to the input features which it represents. In this way, filters learn to become more distinct which increases the diversity of the extracted features. When replacing standard convolutional layers with the proposed layers the performance of classification networks is not only improved on ImageNet but also significantly boosted on eight robustness benchmarks, as well as on downstream detection and segmentation tasks. Most notably, ResNet50/101/152 robust accuracy increases by 15.9%/20.0%/20.9% under FGSM attack, and by 10.5%/14.7%/15.0% under PGD attack.
Disciplines :
Computer science
Author, co-author :
Gao, Bo ; Department of Intelligent Manufacturing and Electrical Engineering, Nanyang Normal University, Nanyang, China ; The Department of Informatics, King's College London, Strand, London, United Kingdom
SPRATLING, Michael ; University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Behavioural and Cognitive Sciences (DBCS) > Cognitive Science and Assessment ; The Department of Informatics, King's College London, Strand, London, United Kingdom
External co-authors :
yes
Language :
English
Title :
Filter competition results in more robust Convolutional Neural Networks
This research was funded by the special project of Nanyang Normal University, China (Grant Number :2024ZX033). The authors acknowledge use of the research computing facility at King's College London, CREATE [82], and the Joint Academic Data science Endeavour (JADE) facility.This research was funded by the special project of Nanyang Normal University (Grant Number : 2024ZX033 ) . The authors acknowledge use of the research computing facility at King\u2019s College London, CREATE [82] , and the Joint Academic Data science Endeavour (JADE) facility.
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