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 underl...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.展开更多
基金National Natural Science Foundation of China,Grant/Award Number:61936001Natural Science Foundation of Chongqing,Grant/Award Number:cstc2019jcyj-msxmX0380China Postdoctoral Science Foundation,Grant/Award Number:2021M700562。
文摘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.
