Abstract
Almost all Deep Learning models are dramatically affected by Catastrophic Forgetting when learning over continual streams of data. To mitigate this problem, several strategies for Continual Learning have been proposed, even though the extent of the forgetting is still unclear. In this paper, we analyze Concept Bottleneck (CB) models in the Continual Learning setting and we investigate the effect of high-level features supervision on Catastrophic Forgetting at the representation layer. Consequently, we introduce two different metrics to evaluate the loss of information on the learned concepts as new experiences are encountered. We also show that the obtained Saliency maps remain more stable with the attributes supervision. The code is available at https://github.com/Bontempogianpaolo1/continualExplain
E. Marconato and G. Bontempo—Equal contribution.
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Marconato, E., Bontempo, G., Teso, S., Ficarra, E., Calderara, S., Passerini, A. (2022). Catastrophic Forgetting in Continual Concept Bottleneck Models. In: Mazzeo, P.L., Frontoni, E., Sclaroff, S., Distante, C. (eds) Image Analysis and Processing. ICIAP 2022 Workshops. ICIAP 2022. Lecture Notes in Computer Science, vol 13374. Springer, Cham. https://doi.org/10.1007/978-3-031-13324-4_46
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