# OISE: Optimized Input Sampling Explanation with a Saliency Map Based on the Black-Box Model
## 5 Conclusions  
We propose Optimized Input Sampling Explanation (OISE), a new black-box explanation method for visual interpretation. OISE reduces the computational complexity of randomly generated masks by optimizing the loss function, continuously updating the masks, minimizing the area of the generated masks, and influencing the decision scores, and finally generating saliency maps to show the reasons why the model makes the final decision, thus providing a better explanation to the user. We analyze the evaluation and compare the results, and the method outperforms previous CAM-based methods for better visual explanations in multi-objective classification tasks, making machine decisions more transparent and credible. The method can be applied in many domains, such as the XAI problem for visual target detection, where it can be integrated into a model to generate an interpretation of the target detection, i.e., a bounding box. At the detection level, an attention map is computed to evaluate what information leads to a particular decision.  
A good visual explanation will increase people's confidence in the black box model, and with the continuous development of science and technology level, more accurate explainable models can be more widely used in various fields such as medicine, automobiles, and industry to reduce human workload. However, the accuracy of current methods still needs to be improved, which is an important issue that we must continue to explore. In future research on deep learning explainability, we can focus on how to merge different model interpretation techniques to build a more powerful model interpretation method; develop metrics for interpretation methods to measure the interpretation results of models in a more rigorous way; and explore the interpretation work of unsupervised and self-supervised methods to give stronger explainability to models, ensure their fairness, increase privacy protection performance and robustness, and improve users' trust in explainable systems.  
Conceptualization, Z.W.; methodology, Z.W.; software, Z.W.; validation, Z.W. and I.J.; writing--original draft preparation, Z.W.; writing--review and editing, Z.W. and I.J.; visualization, Z.W.; supervision, I.J.; project administration, Z.W. and I.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2020-0-00107, Development of the technology to automate the recommendations for big data analytic models that define data characteristics and problems).  
Not applicable.  
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.  
The authors declare that they have no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} XAI & Explainable Artificial Intelligence \\ OISE & Optimized Input Sampling Explanation \\ AI & Artificial Intelligence \\ RISE & Randomized Input Sampling for Explanation \\ CAM & Class Activation Mapping \\ Grad-CAM & Gradient-weighted Class Activation Mapping \\ CIC & Channel-wise Increase of Confidence \\ LIME & Local Interpretable Model Diagnostic Explanations \\ AUC & Area Under Curve \\ \end{tabular}