Authors:
Sara Kebir
and
Karim Tabia
Affiliation:
Univ. Artois, CNRS, CRIL F-62300 Lens, France
Keyword(s):
Concept Drift, Lightweight Incremental Learning, Calibration, XAI, Feature Attribution.
Abstract:
In many real-world applications, we face two important challenges: The shift in data distribution and the concept drift on the one hand, and on the other hand, the constraints of limited computational resources, particularly in the field of IoT and edge AI. Although both challenges have been well studied separately, it is rare to tackle these two challenges together. In this paper, we put ourselves in a context of limited resources and we address the problem of the concept and distribution shift not only to ensure a good level of accuracy over time, but also we study the impact that this could have on two complementary aspects which are the confidence/calibration of the model as well as the explainability of the predictions in this context. We first propose a global framework for this problem based on incremental learning, model calibration and lightweight explainability. In particular, we propose a solution to provide feature attributions in a context of limited resources. Finally,
we empirically study the impact of incremental learning on model calibration and the quality of explanations.
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