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..
SAViT: Structure-Aware Vision Transformer Pruning via Collaborative Optimization
Authors: Chuanyang Zheng, zheyang li, Kai Zhang, Zhi Yang, Wenming Tan, Jun Xiao, Ye Ren, Shiliang Pu
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the effectiveness of our method. Notably, the proposed approach outperforms the existing state-of-the-art approaches on Image Net, increasing accuracy by 0.7% over the Dei T-Base baseline while saving 50% FLOPs. On COCO, we are the first to show that 70% FLOPs of Faster R-CNN with Vi T backbone can be removed with only 0.3% m AP drop. |
| Researcher Affiliation | Collaboration | Chuanyang Zheng1, Zheyang Li1,2, Kai Zhang1, Zhi Yang1, Wenming Tan1, Jun Xiao2, Ye Ren1, Shiliang Pu1 1 Hikvision Research Institute, Hangzhou, China 2 Zhejiang University, Hangzhou, China |
| Pseudocode | Yes | Algorithm 1 Collaborative Pruning with EA Input: Pre-trained model To, FLOPs constraint Cbudget, dataset D, search iterations E, population size Q, components number M, fitness value f Output: Optimal pruned model |
| Open Source Code | Yes | The code is available at https://github.com/hikvision-research/SAVi T. |
| Open Datasets | Yes | The pruning process is performed on the pre-trained Dei T 1 released from official implementation on Image Net-1k [41]. [...] We employ pruning on the popular object detection framework Faster R-CNN [48] with Swin-Tiny backbone on COCO 2017 dataset [49] and report mean Average Precision (m AP) for comparison. |
| Dataset Splits | No | The paper uses standard datasets like ImageNet-1k and COCO 2017, which have predefined splits, and mentions fine-tuning. However, it does not explicitly provide the specific training/validation/test split percentages or sample counts used in *this* work, nor does it explicitly state that it adheres to a *specific* predefined split from a citation within the context of its own experimental setup. |
| Hardware Specification | Yes | Run time speedup of compressed Dei T on Nvidia V100. |
| Software Dependencies | No | The paper mentions using 'Dei T 1 released from official implementation' and 'official pre-trained Swin2' but does not list specific software dependencies with version numbers (e.g., PyTorch 1.x, TensorFlow 2.x, CUDA 11.x). |
| Experiment Setup | Yes | After pruning, we fine-tune the pruned network using the same setting as Dei T [23] without warm-up. [...] Finally, we fine-tune the pruned network for 300 epochs under the same strategies as Swin [3]. [...] For Dei T-Base, we fine-tune the pruned models for 80 epochs following the identical setting in Section 4.1. |