Prototypical Quadruplet for Few-Shot Class Incremental Learning
Published in Procedia Computer Science, 2023
A prototypical few-shot class-incremental learning framework with contrastive embedding optimization and prototype re-centering for mitigating catastrophic forgetting and improving robust classification under limited data settings
Recommended citation: Sanchar Palit, Subhasis Chaudhuri, and Biplab Banerjee, "Prototypical Quadruplet for Few-Shot Class Incremental Learning", Procedia Computer Science, presented at INNS DLIA, IJCNN 2023
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