umbertocappellazzo / PETL_AST

This is the official repository of the papers "Parameter-Efficient Transfer Learning of Audio Spectrogram Transformers" and "Efficient Fine-tuning of Audio Spectrogram Transformers via Soft Mixture of Adapters".

Date Created 2023-12-02 (about a year ago)

Commits 53 (last one 4 months ago)

Stargazers 37 (0 this week)

Watchers 6 (0 this week)

Forks 3

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RepositoryStats indexes 595,856 repositories, of these umbertocappellazzo/PETL_AST is ranked #535,835 (10th percentile) for total stargazers, and #300,666 for total watchers. Github reports the primary language for this repository as Python, for repositories using this language it is ranked #105,168/119,431.

umbertocappellazzo/PETL_AST is also tagged with popular topics, for these it's ranked: transfer-learning (#289/314), audio-processing (#257/289), lora (#247/278)

Other Information

Homepage URL: https://arxiv.org/abs/2312.03694

All Topics

lora adapter prompt-tuning audio-processing transfer-learning mixture-of-experts mixture-of-adapters audio-spectrogram-transformer parameter-efficient-fine-tuning

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53 commits on the default branch (main) since jan '22

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The only known language in this repository is Python

updated: 2024-12-04 @ 12:55pm, id: 726425219 / R_kgDOK0xegw