# OISE: Optimized Input Sampling Explanation with a Saliency Map Based on the Black-Box Model
## 2 Related Works
The difference between this and CAM is that Grad-CAM adds a ReLU to the final weighted sum, the reason being that we only care about pixel points that have a positive impact on the target class, and without the ReLU layer, we may end up bringing in some pixels that belong to other classes, thus affecting the interpretation. [PERSON], [PERSON] et al. proposed Score-CAM [24], which follows the main idea of CAM (linear weighting of the feature map). Compared with the previous series of CAM methods, the main difference is the way to obtain the linear weights. The first generation of CAM used the model weights on the full connection layer after training. Grad CAM and Grad-CAM++ [25] both used the local gradients on the corresponding feature map (the difference is in the method of processing the gradients). Unlike previous methods based on class activation maps, Score-CAM obtains the weights of each activation map by forward-passing the scores of each activation map on the target class, thus eliminating the dependence on gradients, and the final result is obtained by a linear combination of weights and activation maps. The results of the study show that Score-CAM has better visualization and fairness in explaining the decision process. Score-CAM not only locates a single object accurately, but also shows better performance than previous work in locating multiple objects of the same type. Grad-CAM tends to capture only one target in the image, and both Grad-CAM++ and Score-CAM show the ability to locate multiple targets; however, Score-CAM's remarkable map is more focused than Grad-CAM++. [PERSON] et al. proposed the locally interpretable model diagnostic interpretation (LIME) in 2016 [26]. LIME is an algorithm that interprets the predictions of a classifier or regressor by performing a local approximation with an interpretable model. Itmodifies a single data sample by adjusting the feature values and observing the effect on the output. The output of LIME is a set of interpretations representing the contribution of each feature to the prediction of a single sample, which is a local interpretability. However, the LIME algorithm is slow, and the results must be predicted once by the original model for each sampled image after sampling is complete. In summary, the RISE method generates multiple masks by random or Monte Carlo sampling to calculate the importance of each mask field, which usually requires a lot of masks and calculations. It is very complex and wastes time and resources. The existing CAM series methods have been able to interpret image targets more accurately in terms of localization, but the evaluation metrics need to be artificially estimated and the model architecture needs to be changed, which cannot locate multiple objects or locate them inaccurately. The LIME algorithm has strong generality and does not need to change the model internals, but the result is a local approximation of the black box model instead of a global approximation, and when the input is perturbed, the samples obey the Gaussian distribution, ignore the correlation between features, and is not stable enough to obtain completely different results for repeated interpretations using the same parameters and the same method.