💥💥💞💞欢迎来到本博客❤️❤️💥💥
🏆博主优势：🌞🌞🌞博客内容尽量做到思维缜密，逻辑清晰，为了方便读者。
⛳️座右铭：行百里者，半于九十。
📋📋📋本文目录如下：🎁🎁🎁
目录
💥1 概述
📚2 运行结果
🎉3 参考文献
🌈4 Matlab代码实现
💥1 概述
基于LIME（Local Interpretable Model-Agnostic Explanations）的CNN图像分类研究是一种用于解释CNN模型的方法。LIME是一种解释性模型，旨在提供对黑盒模型（如CNN）预测结果的可解释性。下面是简要的步骤：
1. 数据准备：首先，准备一个用于图像分类的数据集，该数据集应包含图像样本和相应的标签。可以使用已有的公开数据集，如MNIST、CIFAR-10或ImageNet。
2. 训练CNN模型：使用准备好的数据集训练一个CNN模型。可以选择常见的CNN架构，如VGG、ResNet或Inception等，或者根据具体需求设计自定义的CNN架构。
3. 解释模型的预测结果：使用LIME方法来解释CNN模型的预测结果。LIME采用局部特征解释方法，在图像中随机生成一组可解释的超像素，并对这些超像素进行采样。然后，将这些采样结果输入到CNN模型中，计算预测结果。
4. 生成解释性结果：根据LIME采样的结果，计算每个超像素对预测结果的影响程度。可以使用不同的解释性度量，如权重、重要性分数或热图等。
5. 分析和验证结果：对生成的解释性结果进行分析和验证。可以通过与真实标签进行对比或与其他解释方法进行比较，来评估LIME方法的准确性和可靠性。
通过以上步骤，可以实现对CNN图像分类模型的解释性研究。LIME方法可以帮助我们理解CNN模型在图像分类任务中的决策过程，对于深入了解CNN模型的特征选择和预测行为非常有帮助。
📚2 运行结果
result=zeros(size(L));
for i=1:N
ROI=L==i;
result=result+ROI.*max(mdl.Beta(i),0);% calculate the contribution if the weight is non-zero
end% smoothing the LIME result. this is not included in the official
% implementation
result2=imgaussfilt(result,8);
% display the final result
figure;imshow(I);hold on
imagesc(result2,'AlphaData',0.5);
colormap jet;colorbar;hold off;
title("Explanation using LIME");
部分代码：
%% Sampling for Local Exploration
% This section creates pertubated image as shown below. Each superpixel was
% assigned 0 or 1 where the superpixel with 1 is displayed and otherwise colored
% by black.
%
%
% the number of the process to make perturbated images
% higher number of sampleNum leads to more reliable result with higher
% computation cost
sampleNum=1000;
% calculate similarity with the original image
similarity=zeros(sampleNum,1);
indices=zeros(sampleNum,N);
img=zeros(224,224,3,sampleNum);
for i=1:sampleNum
% randomly black-out the superpixels
ind=rand(N,1)>rand(1)*.8;
map=zeros(size(I,1:2));
for j=[find(ind==1)]'
ROI=L==j;
map=ROI+map;
end
img(:,:,:,i)=imresize(I.*uint8(map),[224 224]);
% calculate the similarity
% other metrics for calculating similarity are also fine
% this calculation also affetcts to the result
similarity(i)=1-nnz(ind)./numSuperPixel;
indices(i,:)=ind;
end
%% Predict the perturbated images using CNN model to interpret
% Use |activations| function to explore the classification score for cat.
prob=activations(net,uint8(img),'prob','OutputAs','rows');
score=prob(:,classIdx);
%% Fitting using weighted linear model
% Use fitrlinear function to perform weighted linear fitting. Specify the weight
% like 'Weights',similarity. The input indices represents 1 or 0. For example,
% if the value of the variable "indices" is [1 0 1] , the first and third superpixels
% are active and second superpixel is masked by black. The label to predict is
% the score with each perturbated image. Note that this similarity was calculated
% using Kernel function in the original paper.
sigma=.35;
weights=exp(-similarity.^2/(sigma.^2));
mdl=fitrlinear(indices,score,'Learner','leastsquares','Weights',weights);
%%
% Confirm the exponential kernel used for the weighting.
🎉3 参考文献
部分理论来源于网络，如有侵权请联系删除。
[1] Ribeiro, M.T., Singh, S. and Guestrin, C., 2016, August. " Why should
I trust you?" Explaining the predictions of any classifier. In _Proceedings
of the 22nd ACM SIGKDD international conference on knowledge discovery and data
mining_ (pp. 1135-1144).
[2] He, K., Zhang, X., Ren, S. and Sun, J., 2016. Deep residual learning for
image recognition. In _Proceedings of the IEEE conference on computer vision
and pattern recognition_ (pp. 770-778).
