On the Transferability of Large-Scale Self-Supervision to Few-Shot Audio Classification
<|reference_start|>On the Transferability of Large-Scale Self-Supervision to Few-Shot Audio Classification: In recent years, self-supervised learning has excelled for its capacity to learn robust feature representations from unlabelled data. Networks pretrained through self-supervision serve as effective feature extractors for downstream tasks, including Few-Shot Learning. While the evaluation of unsupervised approaches for few-shot learning is well-established in imagery, it is notably absent in acoustics. This study addresses this gap by assessing large-scale self-supervised models' performance in few-shot audio classification. Additionally, we explore the relationship between a model's few-shot learning capability and other downstream task benchmarks. Our findings reveal state-of-the-art performance in some few-shot problems such as SpeechCommandsv2, as well as strong correlations between speech-based few-shot problems and various downstream audio tasks.<|reference_end|>
@article{heggan2024on,
    title={On the Transferability of Large-Scale Self-Supervision to Few-Shot Audio
  Classification},
    author={Calum Heggan, Sam Budgett, Timothy Hospedales, Mehrdad Yaghoobi},
    journal={arXiv preprint arXiv:2402.01274},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.01274},
    primaryClass={cs.SD cs.LG eess.AS}
}