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 | Conference PDF | Archive PDF | Plain Text | 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. |