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..
USDNL: Uncertainty-Based Single Dropout in Noisy Label Learning
Authors: Yuanzhuo Xu, Xiaoguang Niu, Jie Yang, Steve Drew, Jiayu Zhou, Ruizhi Chen
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive empirical results on both synthetic and real-world datasets show that USDNL outperforms other methods. Our code is available at https: //github.com/kovelxyz/USDNL. The Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI-23) |
| Researcher Affiliation | Academia | Yuanzhuo Xu1, Xiaoguang Niu1,4*, Jie Yang1, Steve Drew2, Jiayu Zhou3, Ruizhi Chen4 1School of Computer Science, Wuhan University, China 2 Department of Electrical and Software Engineering, University of Calgary, Canada 3 Department of Computer Science and Engineering, Michigan State University, USA 4 LIESMARS, Wuhan University, China |
| Pseudocode | Yes | Algorithm 1: The training pipeline of USDNL |
| Open Source Code | Yes | Our code is available at https: //github.com/kovelxyz/USDNL. |
| Open Datasets | Yes | We verify the effectiveness of USDNL on four manually corrupted datasets, i.e., MNIST (Le Cun 1998), CIFAR-10, CIFAR-100 (Krizhevsky 2009) with artificial corruption, along with a real-world noisy dataset Clothing1M (Xiao et al. 2015). |
| Dataset Splits | No | The paper does not explicitly provide specific train/validation/test dataset splits (e.g., percentages or sample counts). It only mentions shuffling the training set. |
| Hardware Specification | Yes | To compare algorithm complexity, we run each algorithm for 5 epochs on the RTX-2080Ti platform and count the mean and standard deviation of a single epoch run. |
| Software Dependencies | No | The paper mentions using specific network architectures (Le Net, 9-CNN, Resnet-18) and a unified dropout rate of 0.25, but it does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Network setting For a fair comparison, different schemes use the same network structure on the same dataset. Specifically, we use a Le Net for MNIST, a 9-CNN network for CIFAR-10 and CIFAR-100, and non-pretrained Resnet-18 for Clothing1M. USDNL adds several dropout layers to the standard network. Without complex parameter tuning on different datasets like other methods, we incorporate a unified dropout rate of 0.25. |