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..
Unified Visual Transformer Compression
Authors: Shixing Yu, Tianlong Chen, Jiayi Shen, Huan Yuan, Jianchao Tan, Sen Yang, Ji Liu, Zhangyang Wang
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments are conducted with several Vi T variants, e.g. Dei T and T2T-Vi T backbones on the Image Net dataset, and our approach consistently outperforms recent competitors. |
| Researcher Affiliation | Collaboration | 1University of Texas at Austin, 2Texas A&M University, 3Kwai Inc. |
| Pseudocode | Yes | Algorithm 1: Gradient-based algorithm to solve problem (5) for Unified Vi T Compression. Input: Resource budget Rbudget, learning rates η1, η2, η3, η4, η5, η6, number of total iterations τ. Result: Transformer pruned weights W . |
| Open Source Code | Yes | Codes are available online: https://github.com/VITA-Group/UVC. |
| Open Datasets | Yes | We conduct experiments for image classification on Image Net (Krizhevsky et al., 2012). |
| Dataset Splits | No | The paper states 'We conduct experiments for image classification on Image Net (Krizhevsky et al., 2012),' and mentions 'validation' in section '3.1 PRELIMINARY' and the JSON schema itself has a 'validation' field, but it does not provide specific details on the dataset splits (e.g., percentages or sample counts for training, validation, and testing). |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware used for experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies such as programming languages or libraries used in the implementation. |
| Experiment Setup | Yes | Numerically, the learning rate for parameter z is always changing during the primal-dual algorithm process. Thurs, we propose to use a dynamic learning rate for the parameter z that controls the budget constraint. We use a four-step schedule of {1, 5, 9, 13, 17} in practice. |