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A Bag of Tricks for Few-Shot Class-Incremental Learning
FSCIL requires both stability and adaptability, i. e., preserving proficiency in previously learned tasks while learning new ones.
Ranked #2 on
Few-Shot Class-Incremental Learning
on CUB-200-2011
Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models
Foundation models (FMs) adapt well to specific domains or tasks with fine-tuning, and federated learning (FL) enables the potential for privacy-preserving fine-tuning of the FMs with on-device local data.
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