Deep neural network feature visualization method for constrained optimization class activation mapping

A deep neural network and constrained optimization technology, which is applied in the field of constrained optimization class activation mapping deep neural network feature visualization, can solve the problems of weak class discrimination and large noise, and achieve strong class discrimination, less noise, and good visual effects Effect

Active Publication Date: 2020-09-22
ZHEJIANG UNIV
View PDF3 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In order to solve the problems existing in the background, the present invention provides a deep neural network feature visualization method base...

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Deep neural network feature visualization method for constrained optimization class activation mapping
  • Deep neural network feature visualization method for constrained optimization class activation mapping
  • Deep neural network feature visualization method for constrained optimization class activation mapping

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0041] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments.

[0042] According to the example that the complete method of content of the present invention implements and its implementation situation are as follows:

[0043] The embodiment uses the deep neural network VGG19 trained on the ImageNet data set as the target model, and is described in detail as follows:

[0044] 1) Obtain a pre-trained model by training or downloading. Torchvision provides a pre-trained VGG19 model on the ImageNet dataset, which can be directly loaded and used.

[0045] 2) Set the feature map to be used, that is, the output of a certain layer of the VGG19 model as the feature map used for subsequent visualization, for example, select the output "features.34" of the last convolutional layer of VGG19.

[0046] 3) For an image X to be tested, such as figure 2 As shown, the input pre-training model is forwarded to obt...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a deep neural network feature visualization method for constraint optimization class activation mapping. The method comprises the steps of obtaining a pre-training model whichis used for image classification and is constructed by adopting a deep neural network through training or downloading; carrying out forward transmission on a to-be-detected image by using the pre-training model to obtain a feature map, and carrying out further processing to obtain a final weight vector; and performing weighted summation on each component of the feature map through the final weightvector to obtain a visual feature map, and presenting the visual feature map as a final visual result. According to the method, feature visualization can be carried out on any deep neural network, abetter deep feature interpretability visualization effect can be achieved, and the method has less noise and stronger class discrimination.

Description

technical field [0001] The invention relates to an image feature visualization method in the field of deep learning interpretability, in particular to a deep neural network feature visualization method for constrained optimization and class activation mapping. Background technique [0002] Deep learning techniques have achieved remarkable results and superior performance in some fields, especially in the field of computer vision, such as image classification and other tasks. However, because its mathematical principles have not been fully proven, its end-to-end black-box nature makes it impossible for humans to know how a deep neural network makes decisions. Therefore, research on the interpretability of deep learning has gradually emerged in recent years. One of the most direct ideas is to use visualization technology to obtain image regions that play a positive role in prediction, especially to visualize the feature representation of the middle layer of deep neural network...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06K9/62G06K9/46G06N3/04G06N3/08
CPCG06N3/08G06V10/40G06N3/045G06F18/2411
Inventor 孔祥维王鹏达
Owner ZHEJIANG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products