update README: add general description

Author: KentaItakura

README.md

@@ -1,15 +1,24 @@
 # Explainable AI: interpreting the classification using score-CAM
 
-This example shows how to use score-CAM (class activation mapping) [1] to investigate and explain the predictions of a deep convolutional neural network for image classification. This code was created with reference to Matlab official document [2]. 
+**[English]**
 
-[1] Wang, H., Du, M., Yang, F. and Zhang, Z., 2019. Score-CAM: Improved Visual Explanations Via Score-Weighted Class Activation Mapping. *arXiv preprint arXiv:1910.01279*.
+This example shows how to use score-CAM (class activation mapping) [1] to investigate and explain the predictions of a deep convolutional neural network for image classification. The algorithm like CAM [2] and grad-CAM [3] are popular for explainable AI with image classification, but Score-CAM proposed a "gradient-free" method for the explanation. [The original implementation can be found here](https://github.com/haofanwang/Score-CAM). 
+
+**[Japanese]**
+
+このスクリプトでは、畳み込み込みニューラルネットワーク（CNN）を用いて画像分類を行ったときの、その分類の判断根拠の可視化を行います。Score-CAMと呼ばれる手法を実装したいと思います。[著者らによるオリジナルの実装のページはこちら](https://github.com/haofanwang/Score-CAM)です。
 
-[2] MATLAB documentation: "[Investigate Network Predictions Using Class Activation Mapping](https://jp.mathworks.com/help/deeplearning/ug/investigate-network-predictions-using-class-activation-mapping.html)"  
+なお、本内容は、[こちらのブログ](https://kentapt.hatenablog.com/entry/2021/12/16/105958)でより詳しく、日本語で書かれています。よろしければぜひご覧ください。
 
-[3] Zhou, B., Khosla, A., Lapedriza, A., Oliva, A. and Torralba, A., 2016. Learning deep features for discriminative localization. In *Proceedings of the IEEE conference on computer vision and pattern recognition* (pp. 2921-2929).  
+  
+
+**Keywords**: classification, deep learning, explainable AI, gradient, Score-CAM
+
+[1] Wang, H., Du, M., Yang, F. and Zhang, Z., 2019. Score-CAM: Improved Visual Explanations Via Score-Weighted Class Activation Mapping. *arXiv preprint arXiv:1910.01279*.
 
-**The official implementation of Score-CAM is located [here](https://github.com/haofanwang/Score-CAM).**
+[2] Zhou, B., Khosla, A., Lapedriza, A., Oliva, A. and Torralba, A., 2016. Learning deep features for discriminative localization. In *Proceedings of the IEEE conference on computer vision and pattern recognition* (pp. 2921-2929). 
 
+[3] [Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D. and Batra, D., 2017. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision (pp. 618-626).](https://openaccess.thecvf.com/content_iccv_2017/html/Selvaraju_Grad-CAM_Visual_Explanations_ICCV_2017_paper.html)
 
 # Load Pretrained Network 
 
@@ -36,14 +45,15 @@ layerName = activationLayerName(netName);
 Compute the activations of the resized image in the ReLU layer that follows the last convolutional layer of the network.
 
 ```matlab:Code
+addpath('testImg\')
 im = imread('CatImg.png');
 imResized = imresize(im,[inputSize(1:2)]);
 ```
 
 # Classify the target image into a category 
 
 ```matlab:Code
-[PredCategory,~]=classify(net,imResized);
+[PredCategory,scoreBaseLine]=classify(net,imResized);
 PredCategory
 ```
 
@@ -106,16 +116,19 @@ Use `classify` function to predict and extract the score for the target class
 
 ```matlab:Code
 % specify minibatch size. Return an error if the memory is not enough   
+% score: (the number of test image)-by-(the number of class (1000))
 [PredCategory,score]=classify(net,uint8(maskedInputImg),'MiniBatchSize',32);
 score_target_class=score(:,classIdx);
+CIC=score_target_class-scoreBaseLine(classIdx);
+CIC_norm=softmax(CIC);
 ```
 
 # Multiply the featuremap with the score with corresponding input mask image 
 
 Generate course heat map for your final result. The size of the course map is identical to the size of the feature map.  
 
 ```matlab:Code
-score_CAM_prep=featureMap_normalized.*reshape(score_target_class,[1 1 numel(score_target_class)]);
+score_CAM_prep=featureMap_normalized.*reshape(CIC_norm,[1 1 numel(CIC_norm)]);
 score_CAM_sum=sum(score_CAM_prep,3);
 ```