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 (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:this https URL

@article{raichur2025_2405.11067,
  title={ Bayesian Learning-driven Prototypical Contrastive Loss for Class-Incremental Learning },
  author={ Nisha L. Raichur and Lucas Heublein and Tobias Feigl and Alexander Rügamer and Christopher Mutschler and Felix Ott },
  journal={arXiv preprint arXiv:2405.11067},
  year={ 2025 }
}