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

• URL: http://arxiv.org/abs/2405.11067v3
• Date: Mon, 31 Mar 2025 13:04:03 GMT
• Title: Bayesian Learning-driven Prototypical Contrastive Loss for Class-Incremental Learning
• Authors: Nisha L. Raichur, Lucas Heublein, Tobias Feigl, Alexander Rügamer, Christopher Mutschler, Felix Ott,
• Abstract summary: This paper proposes a method to learn an effective representation between previous and newly encountered class prototypes.<n>We introduce a contrastive loss that incorporates novel classes into the latent representation by reducing intra-class and increasing inter-class distance.
• Score: 42.14439854721613
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The primary objective of methods in continual learning is to learn tasks in a sequential manner over time (sometimes from a stream of data), while mitigating the detrimental phenomenon of catastrophic forgetting. This paper proposes a method to learn an effective representation between previous and newly encountered class prototypes. We propose a prototypical network with a Bayesian learning-driven contrastive loss (BLCL), tailored specifically for class-incremental learning scenarios. We introduce a contrastive loss that incorporates novel classes into the latent representation by reducing intra-class and increasing inter-class distance. Our approach dynamically adapts the balance between the cross-entropy and contrastive loss functions with a Bayesian learning technique. Experimental results conducted on the CIFAR-10, CIFAR-100, and ImageNet100 datasets for image classification and images of a GNSS-based dataset for interference classification validate the efficacy of our method, showcasing its superiority over existing state-of-the-art approaches. Git: https://gitlab.cc-asp.fraunhofer.de/darcy_gnss/gnss_class_incremental_learning

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