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..
Res-Tuning: A Flexible and Efficient Tuning Paradigm via Unbinding Tuner from Backbone
Authors: Zeyinzi Jiang, Chaojie Mao, Ziyuan Huang, Ao Ma, Yiliang Lv, Yujun Shen, Deli Zhao, Jingren Zhou
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on both discriminative and generative tasks demonstrate the superiority of our method over existing alternatives from the perspectives of efficacy and efficiency. |
| Researcher Affiliation | Collaboration | 1Alibaba Group 2National University of Singapore 3Ant Group |
| Pseudocode | No | The paper includes equations and architectural diagrams but does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Project page: https://res-tuning.github.io/. |
| Open Datasets | Yes | For most experiments, we adopt Vi T-B/16 [13] pre-trained on Image Net-21K [11] as the backbone model, following VPT [28]. ... We evaluate the text-to-image generation performance on COCO2017 dataset [43]. ... Table 8: Datasets used for generative tasks. ... Table 9: Datasets used for discriminative tasks. |
| Dataset Splits | No | The paper mentions using 'validation set' for evaluation (e.g., 'combine the validation set of 19 tasks in VTAB-1K' or 'sample 10k captions from the validation set'), but it does not consistently provide specific train/validation/test dataset splits with percentages or counts for all experiments. |
| Hardware Specification | Yes | Device A100 1 (for discriminative tasks), Device A100 8 (for generative tasks) |
| Software Dependencies | Yes | Library Diffusers 2 |
| Experiment Setup | Yes | Table 10: Hyperparameter selection for discriminative tasks. ... Table 11: Hyperparameter selection for generative tasks. |