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nviv

Although deep neural networks have demonstrated greater performance in a variety of tasks, their unexplainability and untrustworthiness exist concurrently, which seriously hinders the further development of decision-making,  particularly in high-risk areas. This work presents Node Visualization and Interpretability Verification (NVIV), a technique of DNN interpretability verification, to solve these difficulties. This method offers two unique benefits: 1) This technique replicates the DNN decision-making process using a decision tree and visualizes node output to give a basis for decision-making; 2) An interpretability verification method based on the correlation degree of convolution kernel units is presented to assess the model's confidence. Our method not only achieves intuitive and easily understandable interpretability, but also achieves high accuracy. In the experiment, we observed that the effect is better than other similar methods by attention force ratio under the positioning evaluation. Furthermore, rather than being limited to the visual visualization zone, the experiments show that the method provided in this study correctly locates the most responsive region in the target item and explains the model's internal decision-making basis. In compared to other similar techniques, the proposed model more accurately describes the decision-making base of DNNs.

ocbnn-public

General purpose library for BNNs, and implementation of OC-BNNs in our 2020 NeurIPS paper.

pytorch-cnn-visualizations

Pytorch implementation of convolutional neural network visualization techniques

pytorch-grad-cam

Many Class Activation Map methods implemented in Pytorch for CNNs and Vision Transformers. Including Grad-CAM, Grad-CAM++, Score-CAM, Ablation-CAM and XGrad-CAM

pytorch1.0-cn

PyTorch 1.0 官方文档 中文版，欢迎关注微信公众号：磐创AI

score-cam

[CVPRW 2020] Official implementation of Score-CAM in Pytorch

segnbdt

Making high-accuracy and visually-interpretable decision tree-based models for semantic segmentation http://segnbdt.aaalv.in

semantic_segmentation_fcn

siyu-home

siyu-vuepress-homepage

teachyourselfcs-cn

TeachYourselfCS 的中文翻译 | A Chinese translation of TeachYourselfCS

test

项目描述

torch-cam

Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)

vue-cms

xai-aaai2022