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..
Stimulative Training of Residual Networks: A Social Psychology Perspective of Loafing
Authors: Peng Ye, Shengji Tang, Baopu Li, Tao Chen, Wanli Ouyang
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive empirical results and theoretical analyses verify that stimulative training can well handle the loafing problem, and improve the performance of a residual network by improving the performance of its sub-networks. |
| Researcher Affiliation | Collaboration | Peng Ye1 , Shengji Tang1 , Baopu Li2 , Tao Chen1 , Wanli Ouyang3 1School of Information Science and Technology, Fudan University, 2Oracle Health and AI, USA, 3The University of Sydney, Sense Time Computer Vision Group, Australia, and Shanghai AI Lab |
| Pseudocode | Yes | The pseudo code is shown in Appendix C.1. |
| Open Source Code | Yes | The code is available at https://github.com/Sunshine-Ye/NIPS22-ST. |
| Open Datasets | Yes | CIFAR[34] is a classical image classification dataset consisting of 50,000 training images and 10,000 testing images. It includes CIFAR-100 in 100 categories and CIFAR-10 in 10 categories. Image Net[36] dataset containing 1.2 million training images and 50,000 validation images from 1,000 categories. |
| Dataset Splits | Yes | CIFAR[34] is a classical image classification dataset consisting of 50,000 training images and 10,000 testing images. It includes CIFAR-100 in 100 categories and CIFAR-10 in 10 categories. Image Net implementation details. We implement our method on large-scale Image Net[36] dataset containing 1.2 million training images and 50,000 validation images from 1,000 categories. |
| Hardware Specification | No | The paper mentions 'computation cost' but does not specify the type of GPUs, CPUs, or other hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'SGD optimizer' but does not specify particular software libraries or frameworks with version numbers (e.g., PyTorch 1.9, TensorFlow 2.x, or a specific Python version). |
| Experiment Setup | Yes | For Mobile Net V3 and Res Net50, the data augmentations follow [35], we use SGD optimizer and train the model for 500 epochs with a batch size of 64. The initial learning rate is 0.05 with cosine decay schedule. The weight decay is 3 10 5 and momentum is 0.9. We utilize SGD optimizer to train the model for 100 epochs with a batch size of 512, and the learning rate is 0.2 with cosine decay schedule. The weight decay is 1 10 4 and momentum is 0.9. |