Publisher
Springer International Publishing
Reference32 articles.
1. Holzinger, A., Langs, G., Denk, H., Zatloukal, K., Muller, H.: Causability and explainability of artificial intelligence in medicine. WIREs Data Min. Knowl. Discov. 9, e1312 (2019)
2. Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., Pedreschi, D.: A survey of methods for explaining black box models. ACM Comput. Surv. 51, 93 (2019)
3. Liang, Y., Li, S., Yan, C., Li, M., Jiang, C.: Explaining the black-box model: a survey of local interpretation methods for deep neural networks. Neurocomputing 419, 168–182 (2021)
4. Xie, N., Ras, G., van Gerven, M., Doran, D.: Explainable deep learning: a field guide for the uninitiated (2020). arXiv:2004.14545. Accessed 24 Mar 2021
5. Hendricks, L.A., Hu, R., Darrell, T., Akata, Z.: Grounding visual explanations. In: Ferrari V., Hebert M., Sminchisescu C., Weiss Y. (eds.) Proceedings of the 15th European Conference on Computer Vision – ECCV 2018, Munich, Germany, September 8–14, 2018, Part II. Lecture Notes in Computer Science, vol. 11206, pp. 269–286. Springer, Cham (2018).https://doi.org/10.1007/978-3-030-01216-8_17