Rethinking the image feature biases exhibited by deep convolutional neural network models in image recognition  

作　　者：Dawei Dai Yutang Li Yuqi Wang Huanan Bao Guoyin Wang 

机构地区：[1]College of Computer Science and Technology,Chongqing University of Posts and Telecommunications,Chongqing,China

出　　处：《CAAI Transactions on Intelligence Technology》2022年第4期721-731,共11页智能技术学报（英文）

基　　金：National Natural Science Foundation of China,Grant/Award Number:61936001;Natural Science Foundation of Chongqing,Grant/Award Number:cstc2019jcyj-msxmX0380;China Postdoctoral Science Foundation,Grant/Award Number:2021M700562。

摘　　要：In recent years,convolutional neural networks(CNNs)have been applied successfully in many fields.However,these deep neural models are still considered as“black box”for most tasks.One of the fundamental issues underlying this problem is understanding which features are most influential in image recognition tasks and how CNNs process these features.It is widely believed that CNN models combine low‐level features to form complex shapes until the object can be readily classified,however,several recent studies have argued that texture features are more important than other features.In this paper,we assume that the importance of certain features varies depending on specific tasks,that is,specific tasks exhibit feature bias.We designed two classification tasks based on human intuition to train deep neural models to identify the anticipated biases.We designed experiments comprising many tasks to test these biases in the Res Net and Dense Net models.From the results,we conclude that(1)the combined effect of certain features is typically far more influential than any single feature;(2)in different tasks,neural models can perform different biases,that is,we can design a specific task to make a neural model biased towards a specific anticipated feature.

关 键 词：CNNS FEATURES understandable models 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]