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..
Towards Fully Sparse Training: Information Restoration with Spatial Similarity
Authors: Weixiang Xu, Xiangyu He, Ke Cheng, Peisong Wang, Jian Cheng2929-2937
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Evaluation of accuracy and efficiency shows that we can achieve 2 training acceleration with negligible accuracy degradation on challenging large-scale classification and detection tasks.In this section, we evaluate the proposed FST in terms of accuracy and efficiency. Our experiments are conducted on image classification and object detection. |
| Researcher Affiliation | Academia | Weixiang Xu1,2, Xiangyu He1,2, Ke Cheng1,2, Peisong Wang1, Jian Cheng1 1NLPR, Institute of Automation, Chinese Academy of Sciences 2School of Artificial Intelligence, University of Chinese Academy of Sciences EMAIL, EMAIL |
| Pseudocode | No | The paper describes its methods in prose but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code for the methodology or provide a link to a code repository. |
| Open Datasets | Yes | To verify the effectiveness of our method, we first evaluate it on the large-scale Image Net.The PASCAL VOC dataset contains around 16k training images with 20 different classes, while the COCO dataset consists of about 80k training images from 80 different categories. |
| Dataset Splits | No | The paper mentions training details like 'batch size 256 for 120 epochs' but does not explicitly state the dataset splits (e.g., percentage for training, validation, and test sets). |
| Hardware Specification | Yes | The execution environment is as below: Tesla A100 GPU 1, Py Torch 1.7, CUDA 11.1. |
| Software Dependencies | Yes | The execution environment is as below: Tesla A100 GPU 1, Py Torch 1.7, CUDA 11.1. |
| Experiment Setup | Yes | We follow hyperparameter settings as (Zhou et al. 2021): all models are trained with batch size 256 for 120 epochs, and learning rate is annealed from 0.1 to 0 with a cosine scheduler. In order to reproduce their reported accuracy, we set weight decay as 7e-5 and use label smooth. |