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We investigate the effectiveness of Explainable AI (XAI) in verifying Machine Unlearning (MU) within the context of harbor front monitoring, focusing on data privacy and regulatory compliance.
1911
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Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.R., Samek, W.: On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PloS one 10
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Ribeiro, M.T., Singh, S., Guestrin, C.: " why should i trust you?" explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD. pp. 1135–1144 (2016)
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Chattopadhay, A., Sarkar, A., Howlader, P., Balasubramanian, V.N.: Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks. In: 2018 IEEE WACV. pp. 839–847. IEEE (2018)
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Dhurandhar, A., Chen, P.Y., Luss, R., Tu, C.C., Ting, P., Shanmugam, K., Das, P.: Explanations based on the missing: Towards contrastive explanations with pertinent negatives. NeurIPS 31
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Kemker, R., McClure, M., Abitino, A., Hayes, T., Kanan, C.: Measuring catastrophic forgetting in neural networks. AAAI 32
2018
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Feghahati, A., Shelton, C.R., Pazzani, M.J., Tang, K.: Cdeepex: Contrastive deep explanations. In: ECAI 2020, pp. 1143–1151. IOS Press (2020)
2020
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Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. International journal of computer vision 128
2020
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Wang, H.a.e.a.: Score-cam: Score-weighted visual explanations for convolutional neural networks. In: IEEE/CVF CVPR-W. pp. 24–25 (2020)
2020
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Bourtoule, L., Chandrasekaran, V., Choquette-Choo, C.A., Jia, H., Travers, A., Zhang, B., Lie, D., Papernot, N.: Machine unlearning. In: 2021 IEEE Symposium on Security and Privacy (SP). pp. 141–159 (2021)
2021
Cited alongside, same era.
Golatkar, A., Achille, A., Ravichandran, A., Polito, M., Soatto, S.: Mixed-privacy forgetting in deep networks. In: 2021 IEEE/ CVF CVPR. pp. 792–801 (2021)
2021
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Muddamsetty, S.M., Jahromi, M.N., Ciontos, A.E., Fenoy, L.M., Moeslund, T.B.: Visual explanation of black-box model: Similarity difference and uniqueness (SIDU) method. Pattern recognition 127
2022
Later among the works it cites.
2022
Later among the works it cites.
Jia, J., Liu, e.a.: Model sparsity can simplify machine unlearning. In: NeurIPS. vol. 36, pp. 51584–51605. Curran Associates, Inc. (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
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2021
Cited alongside, same era.
Nikolov, I.A., Philipsen, M.P., Liu, J., Dueholm, J.V., Johansen, A.S., Nasrollahi, K., Moeslund, T.B.: Seasons in drift: A long-term thermal imaging dataset for studying concept drift. In: NeurIPS. vol. 36. NeurIPS Foundation (2021)
2021
Cited alongside, same era.
2022
Cited alongside, same era.
Colin, J., Fel, T., Cadène, R., Serre, T.: What i cannot predict, i do not understand: A human-centered evaluation framework for explainability methods. NeurIPS 35
2022
Cited alongside, same era.
2023
Later among the works it cites.
Jahromi, M.N., Muddamsetty, S.M., Jarlner, A.S.S., Høgenhaug, A.M., Gammeltoft-Hansen, T., Moeslund, T.B.: Sidu-txt: An XAI Algorithm for NLP with a holistic assessment approach. Natural Language Processing Journal 7
2024
Closest in time.
2024
Closest in time.
Xu, J., Wu, Z., Wang, C., Jia, X.: Machine unlearning: Solutions and challenges. IEEE Transactions on Emerging Topics in Computational Intelligence (2024)
2024
Closest in time.