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..
Chasing Sparsity in Vision Transformers: An End-to-End Exploration
Authors: Tianlong Chen, Yu Cheng, Zhe Gan, Lu Yuan, Lei Zhang, Zhangyang Wang
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive results on Image Net with diverse Vi T backbones validate the effectiveness of our proposals which obtain significantly reduced computational cost and almost unimpaired generalization. |
| Researcher Affiliation | Collaboration | Tianlong Chen1, Yu Cheng2, Zhe Gan2, Lu Yuan2, Lei Zhang3, Zhangyang Wang1 1University of Texas at Austin, 2Microsoft Corporation, 3International Digital Economy Academy |
| Pseudocode | Yes | Algorithm 1 Sparse Vi T Co-Exploration (SVi TE+). Initialize: Vi T model f W , Dataset D, Sparsity distribution S = {s1, , s L}, Update schedule { T, Tend, α, fdecay}, Learning rate η 1: Initialize f W with random sparsity S Highly reduced parameter count. 2: for each training iteration t do 3: Sampling a batch bt D 4: Scoring the input token embeddings and selecting the top-k informative tokens Token selection 5: if (t mod T == 0) and t < Tend then 6: for each layer l do 7: ρ = fdecay(t, α, Tend) (1 sl) Nl 8: Performing prune-and-grow with portion ρ w.r.t. certain criterion, generating masks mprune and mgrow to update f W s sparsity patterns Connectivity exploration 9: end for 10: else 11: W = W η W Lt Updating Weights 12: end if 13: end for 14: return a sparse Vi T with a trained token selector |
| Open Source Code | Yes | Our codes are available at https: //github.com/VITA-Group/SVi TE. |
| Open Datasets | Yes | Our experiments are conducted on Image Net with Dei TTiny/Small/Base backbones. |
| Dataset Splits | Yes | Our experiments are conducted on Image Net with Dei TTiny/Small/Base backbones. The detailed training configurations are listed in Table 1, which mainly follows the default setups in [2]. |
| Hardware Specification | No | The paper mentions 'CUDA benchmark mode' but does not specify the exact hardware (GPU/CPU models, memory) used for the experiments. |
| Software Dependencies | No | The paper mentions 'PyTorch-like style' and 'CUDA benchmark mode' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Table 1: Details of training configurations in our experiments, mainly following the settings in [2]. Backbone Update Schedule { T, Tend, α, fdecay} Batch Size Epochs Inherited Settings from Dei T [2] Dei T-Tiny {20000, 1200000, 0.5, cosine} 512 600 Adam W, 0.0005 batchsize 512 , cosine decay warmup 5 epochs, 0.05 weight decay 0.1 label smoothing, augmentations, etc. |