Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Expanding Sparse Tuning for Low Memory Usage
Authors: Shufan Shen, Junshu Sun, Xiangyang Ji, Qingming Huang, Shuhui Wang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on multiple downstream tasks show that SNELL achieves state-of-the-art performance with low memory usage, endowing PEFT with sparse tuning to large-scale models. |
| Researcher Affiliation | Academia | 1Key Lab of Intell. Info. Process., Inst. of Comput. Tech., CAS 2University of Chinese Academy of Sciences 3Tsinghua University 4Peng Cheng Laboratory |
| Pseudocode | No | The paper does not contain pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Codes are available at https://github.com/ssfgunner/SNELL. |
| Open Datasets | Yes | We evaluate our methods on 24 downstream tasks categorized into two groups following SPT [22]. (i) FGVC [30] is a benchmark for fine-grained image classification. ... (ii) VTAB-1k [59] is a large-scale transfer learning benchmark consisting of 19 visual classification tasks. |
| Dataset Splits | Yes | We follow the validation splits in [22] if the official validation set is unavailable. |
| Hardware Specification | Yes | Table A14: Training time cost on Vi T-B/16 of different PEFT methods using NVIDIA Ge Force RTX 4090 GPU. |
| Software Dependencies | No | Following SPT [22], we use the Adam W optimizer [40] with cosine learning rate decay. ... No specific version numbers for software dependencies are provided. |
| Experiment Setup | Yes | The batch size, learning rate, and weight decay are 32, 1e 3, and 1e 4, respectively. We also follow SPT [22] to implement the standard data augmentation pipeline for VTAB-1K and follow SSF [35] for FGVC as well. |