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..
SIGUA: Forgetting May Make Learning with Noisy Labels More Robust
Authors: Bo Han, Gang Niu, Xingrui Yu, Quanming Yao, Miao Xu, Ivor Tsang, Masashi Sugiyama
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate that SIGUA successfully robustifies two typical base learning methods, so that their performance is often significantly improved. |
| Researcher Affiliation | Collaboration | 1Hong Kong Baptist University 2RIKEN 3AAII, University of Technology Sydney 44Paradigm Inc. (Hong Kong) 5University of Queensland 6The University of Tokyo. |
| Pseudocode | Yes | Algorithm 1 SIGUA-prototype (in a mini-batch). |
| Open Source Code | No | The paper does not provide any concrete access information, such as a repository link or an explicit statement of code release, for the methodology described. |
| Open Datasets | Yes | We verify the effectiveness of SIGUASL and SIGUABC on noisy MNIST, CIFAR-10, CIFAR-100 and NEWS following Han et al. (2018b). |
| Dataset Splits | No | The paper mentions 'validation data' for tuning hyperparameters and discusses 'Test Accuracy', but it does not specify explicit train/validation/test dataset splits (e.g., exact percentages or sample counts). |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running experiments. |
| Software Dependencies | No | The paper mentions using 'Py Torch' as a deep learning framework but does not specify its version number or any other software dependencies with version details. |
| Experiment Setup | Yes | In SET1, O is Adam (Kingma & Ba, 2015) in its default,3 and the number of epochs is 200 with batch size nb as 128; the learning rate is linearly decayed to 0 from epoch 80 to 200. We set γ = 0.01 for all cases, except that γ = 0.001 for pair-45% of MNIST. ... SET2 is a bit complicated: for MNIST, O is Adam with betas as (0.9, 0.1), and lr is divided by 10 every 10 epochs; for CIFAR-10, O is SGD with momentum as 0.9, and lr is divided by 10 every 20 epochs; other hyperparameters have the same values as in SET1. We simply set γ = 1.0 for all cases. |