Bayesian Learning-driven Prototypical Contrastive Loss for Class-Incremental Learning

The primary objective of methods in continual learning is to learn tasks in a sequential manner over time from a stream of data, while mitigating the detrimental phenomenon of catastrophic forgetting. In this paper, we focus on learning an optimal representation between previous class prototypes and...

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Hauptverfasser:	Raichur, Nisha L, Heublein, Lucas, Feigl, Tobias, Rügamer, Alexander, Mutschler, Christopher, Ott, Felix
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Artificial Intelligence Computer Science - Computer Vision and Pattern Recognition
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