Combating Noise: Semi-supervised Learning by Region Uncertainty Quantification
Authors: Zhenyu Wang, Ya-Li Li, Ye Guo, Shengjin Wang
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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 {wangzy20, guo-y18}@mails.tsinghua.edu.cn {liyali13, wgsgj}@tsinghua.edu.cn |
| 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 first, and changes to 0.8 after the first decay of learning rate. C is set to 15 and q is set to 0.1. |