💥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。
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[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
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