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..
Augmented Shortcuts for Vision Transformers
Authors: Yehui Tang, Kai Han, Chang Xu, An Xiao, Yiping Deng, Chao Xu, Yunhe Wang
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on benchmark datasets demonstrate the effectiveness of the proposed method, which brings about 1% accuracy increase of the state-of-the-art visual transformers without obviously increasing their parameters and FLOPs. |
| Researcher Affiliation | Collaboration | 1Key Lab of Machine Perception (MOE), Dept. of Machine Intelligence, Peking University. 2Huawei Noah s Ark Lab. 4Central Software Institution, Huawei Technologies. 3School of Computer Science, Faculty of Engineering, University of Sydney. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The model architecture can be found in the Mind Spore model zoo 4. 4https://gitee.com/mindspore/models/tree/master/research/cv/augvit |
| Open Datasets | Yes | Image Net (ILSVRC-2012) dataset [6] contains 1.3 M training images and 50k validation images from 1000 classes, which is a widely used image classification benchmark. |
| Dataset Splits | Yes | Image Net (ILSVRC-2012) dataset [6] contains 1.3 M training images and 50k validation images from 1000 classes, which is a widely used image classification benchmark. |
| Hardware Specification | Yes | All experiments are conducted with Py Torch [28] and Mind Spore 3 on NVIDIA V100 GPUs. |
| Software Dependencies | No | The paper mentions using 'Py Torch' and 'Mind Spore' but does not specify their version numbers. |
| Experiment Setup | Yes | Specifically, the model is trained with Adam W [24] optimizer for 300 epochs with batchsize 1024. The learning rate is initialized to 10 3 and then decayed with the cosine schedule. Label smoothing [30], Drop Path [21] and repeated augmentation [18] are also implemented following Dei T [34]. The data augmentation strategy contains Rand-Augment [5], Mixup [42] and Cut Mix [41]. |