Mamba-FSCIL: Selective State Space Models for Few-Shot Class-Incremental Learning

Researchers have developed Mamba-FSCIL, a novel approach to few-shot class-incremental learning that utilizes Selective State Space Models (SSMs). This method addresses the challenge of balancing static and dynamic architectures in sequential learning by employing input-dependent parameters for dynamic adaptation. Mamba-FSCIL introduces a dual selective SSM projector to decouple base and novel-class processing and a class-sensitive selective scan mechanism to minimize disruption to existing knowledge while adapting to new classes. Experiments on benchmark datasets like miniImageNet and CIFAR-100 show that Mamba-FSCIL achieves state-of-the-art performance. AI

IMPACT Introduces a novel method for incremental learning that could improve AI's ability to adapt to new information without forgetting previous knowledge.

RANK_REASON This is a research paper detailing a new method for few-shot class-incremental learning. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• CIFAR-100
• Mamba-FSCIL
• miniImageNet
• Selective State Space Models

• paper
• model release

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