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..
FacT: Factor-Tuning for Lightweight Adaptation on Vision Transformer
Authors: Shibo Jie, Zhi-Hong Deng
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On VTAB-1K benchmark, our method performs on par with NOAH, the state-of-the-art PETL method, while being 5 more parameter-efficient. We also present a tiny version that only uses 8K (0.01% of Vi T s parameters) trainable parameters but outperforms full fine-tuning and many other PETL methods such as VPT and Bit Fit. In fewshot settings, Fac T also beats all PETL baselines using the fewest parameters, demonstrating its strong capability in the low-data regime. |
| Researcher Affiliation | Academia | Shibo Jie, Zhi-Hong Deng* School of Intelligence Science and Technology, Peking University EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We use VTAB-1K benchmark (Zhai et al. 2019) to evaluate the performance of our methods in terms of PETL. VTAB-1K consists of 19 different visual classification datasets, which can be divided into three groups: Natural, Specialized, and Structured. Each dataset only contains 1,000 training samples. |
| Dataset Splits | No | The paper mentions 1,000 training samples and reports results on 'test sets', but it does not specify a distinct validation set split or percentages for training, validation, and test splits needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, memory specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'Adam W optimizer' but does not provide specific software dependencies like library names with version numbers (e.g., PyTorch 1.9, Python 3.8, CUDA 11.1). |
| Experiment Setup | Yes | Following Zhang, Zhou, and Liu (2022), we use Adam W optimizer with a learning rate of 1e-3 and batch size of 64 to train for 100 epochs. The hyper-parameter s is roughly swept from {0.01, 0.1, 1, 10, 100}. |