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 [1].
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels
Authors: Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate that the proposed method is superior to baselines and robust to a broad range of label noise types. |
| Researcher Affiliation | Academia | 1TML Lab, The University of Sydney 2Hong Kong Baptist University 3The University of Melbourne 4University of Science and Technology of China 5RIKEN AIP 6The University of Tokyo |
| Pseudocode | Yes | Algorithm 1 CNLCU Algorithm. 1: Input θ1 and θ2, learning rate η, fixed τ, epoch Tk and Tmax, iteration tmax; |
| Open Source Code | No | The paper does not contain any statement about making the source code available or provide a link to a code repository. |
| Open Datasets | Yes | Datasets. We verify the effectiveness of our method on the manually corrupted version of the following datasets: MNIST (Le Cun et al.), F-MNIST (Xiao et al., 2017), CIFAR-10 (Krizhevsky, 2009), and CIFAR-100 (Krizhevsky, 2009) |
| Dataset Splits | Yes | We leave out 10% of noisy training examples as a validation set. |
| Hardware Specification | Yes | implement all methods with default parameters by Py Torch, and conduct all the experiments on NVIDIA Titan Xp GPUs. |
| Software Dependencies | No | The paper mentions software like PyTorch and the Adam optimizer but does not specify their version numbers, which is required for reproducibility. |
| Experiment Setup | Yes | For all experiments, the Adam optimizer (Kingma & Ba, 2014) (momentum=0.9) is used with an initial learning rate of 0.001, and the batch size is set to 128 and we run 200 epochs. We linearly decay learning rate to zero from 80 to 200 epochs as did in (Han et al., 2018). |