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..
Intriguing Properties of Vision Transformers
Authors: Muhammad Muzammal Naseer, Kanchana Ranasinghe, Salman H Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We systematically study this question via an extensive set of experiments encompassing three Vi T families and provide comparisons with a high-performing convolutional neural network (CNN). |
| Researcher Affiliation | Collaboration | Australian National University, ?Mohamed bin Zayed University of AI, +Stony Brook University, Monash University, Linköping University, University of California, Merced, Yonsei University, r Google Research |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Code: https://git.io/Js15X. |
| Open Datasets | Yes | We consider visual recognition task with models pretrained on Image Net [2]. The effect of occlusion is studied on the validation set (50k images). |
| Dataset Splits | Yes | We consider visual recognition task with models pretrained on Image Net [2]. The effect of occlusion is studied on the validation set (50k images). |
| Hardware Specification | Yes | All the models are trained on 4 V100 GPUs. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x, CUDA x.x). |
| Experiment Setup | Yes | Thus, we train models on SIN without applying any augmentation, label smoothing or mixup. |