Episodic fine-tuning prototypical networks for optimization-based few-shot learning: Application to audio classification
<|reference_start|>Episodic fine-tuning prototypical networks for optimization-based few-shot learning: Application to audio classification: The Prototypical Network (ProtoNet) has emerged as a popular choice in Few-shot Learning (FSL) scenarios due to its remarkable performance and straightforward implementation. Building upon such success, we first propose a simple (yet novel) method to fine-tune a ProtoNet on the (labeled) support set of the test episode of a C-way-K-shot test episode (without using the query set which is only used for evaluation). We then propose an algorithmic framework that combines ProtoNet with optimization-based FSL algorithms (MAML and Meta-Curvature) to work with such a fine-tuning method. Since optimization-based algorithms endow the target learner model with the ability to fast adaption to only a few samples, we utilize ProtoNet as the target model to enhance its fine-tuning performance with the help of a specifically designed episodic fine-tuning strategy. The experimental results confirm that our proposed models, MAML-Proto and MC-Proto, combined with our unique fine-tuning method, outperform regular ProtoNet by a large margin in few-shot audio classification tasks on the ESC-50 and Speech Commands v2 datasets. We note that although we have only applied our model to the audio domain, it is a general method and can be easily extended to other domains.<|reference_end|>
@article{zhuang2024episodic,
    title={Episodic fine-tuning prototypical networks for optimization-based
  few-shot learning: Application to audio classification},
    author={Xuanyu Zhuang (LTCI, IP Paris, S2A, IDS), Geoffroy Peeters (LTCI, IP
  Paris, S2A, IDS), Ga"el Richard (S2A, IDS, LTCI, IP Paris)},
    journal={2024 IEEE International Workshop on Machine Learning for Signal
  Processing (MLSP 2024), Sep 2024, London (UK), United Kingdom},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.05302},
    primaryClass={eess.AS cs.LG cs.MM cs.SD eess.SP}
}