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..
Spatial Ensemble: a Novel Model Smoothing Mechanism for Student-Teacher Framework
Authors: Tengteng Huang, Yifan Sun, Xun Wang, Haotian Yao, Chi Zhang
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the effectiveness of the proposed Spatial-Temporal Smoothing by applying it to the stateof-the-art self-supervised approaches (Mo Co [1] and BYOL [2]) and semi-supervised method (Fix Match [7]). We use the of๏ฌcial implementation of Mo Co2 and re-implement BYOL and Fix Match using Pytorch [30]. All experiments are conducted on a machine with 8 RTX2080 GPUs. |
| Researcher Affiliation | Collaboration | Megvii Technology EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Pseudocode of SE. Algorithm 2 Pseudo code of STS. |
| Open Source Code | Yes | Codes and models are available at: https://github.com/tengteng95/Spatial_ Ensemble. |
| Open Datasets | Yes | All the ablation experiments are conducted on the Image Net dataset [36] and trained for 200 epochs unless noted otherwise. ... We use CIFAR-10 and Ci FAR-100 as the benchmark datasets. |
| Dataset Splits | No | The paper mentions common benchmark datasets like ImageNet and CIFAR-10/100 and states it follows the training and evaluation settings of original papers, but does not explicitly provide specific train/validation/test dataset split percentages or sample counts within its own text. |
| Hardware Specification | Yes | All experiments are conducted on a machine with 8 RTX2080 GPUs. |
| Software Dependencies | No | The paper mentions using 'Pytorch' but does not specify its version number or any other software dependencies with their respective version numbers. |
| Experiment Setup | Yes | The masking probability p is set to 0.7/0.5/0.5 for BYOL/Mo Co/Fix Match, respectively. ... The initial learning rate is set to 0.03 and adjusted by cosine learning rate scheduler [32]. Following the original paper, we train the model using SGD with momentum of 0.9, weight decay of 0.0001, and a mini-batchsize of 256. |