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Due to the black-box nature of deep learning models, there is a recent development of solutions for visual explanations of CNNs.
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Alqaraawi, A., Schuessler, M., Weiß, P., Costanza, E., Berthouze, N.: Evaluating saliency map explanations for convolutional neural networks: A user study. p. 275–285. IUI ’20, Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3377325.3377519
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Desai, S., Ramaswamy, H.G.: Ablation-cam: Visual explanations for deep convolutional network via gradient-free localization. In: 2020 IEEE Winter Conference on Applications of Computer Vision (WACV). pp. 972–980 (2020). https://doi.org/10.1109/WACV45572.2020.9093360
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Ferreira, J.J., Monteiro, M.S.: What are people doing about xai user experience? a survey on ai explainability research and practice. In: Marcus, A., Rosenzweig, E. (eds.) Design, User Experience, and Usability. Design for Contemporary Interactive Environments. pp. 56–73. Springer International Publishing, Cham (2020)
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