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..
Combating Noise: Semi-supervised Learning by Region Uncertainty Quantification
Authors: Zhenyu Wang, Ya-Li Li, Ye Guo, Shengjin Wang
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both PASCAL VOC and MS COCO demonstrate the extraordinary performance of our method. Our method achieves the state-of-the-art results on the PASCAL VOC and MS COCO dataset, exceeding supervised baseline methods by 6.2% and 4.2% respectively. |
| Researcher Affiliation | Academia | Zhenyu Wang Yali Li Ye Guo Shengjin Wang Beijing National Research Center for Information Science and Technology (BNRist) Department of Electronic Engineering, Tsinghua University EMAIL EMAIL |
| Pseudocode | No | The paper describes the method through text and equations but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about open-sourcing its code or a direct link to a code repository for its described methodology. |
| Open Datasets | Yes | Dataset. We mainly conduct our proposed method on PASCAL VOC [6] and MS COCO [22]. |
| Dataset Splits | Yes | We mainly adopt four settings: 1) VOC07 trainval (5,011 images) as labeled set and VOC12 trainval (11,540 images) as unlabeled set; 2) VOC07 trainval as labeled set, VOC12 trainval and coco-20cls as unlabeled set; 3) coco-35 as labeled set and coco-80 as labeled set; 4) coco-115 as labeled set and coco-120 as unlabeled set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or specific computing environments with specs) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Pytorch [29] and MMDetection [5]' but does not specify their version numbers, which are necessary for reproducibility. |
| Experiment Setup | Yes | For hyper-parameters, we set b to 0.5. f is set to 0.7 ๏ฌrst, and changes to 0.8 after the ๏ฌrst decay of learning rate. C is set to 15 and q is set to 0.1. |