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..
Asymmetric Loss Functions for Learning with Noisy Labels
Authors: Xiong Zhou, Xianming Liu, Junjun Jiang, Xin Gao, Xiangyang Ji
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on benchmark datasets demonstrate that asymmetric loss functions can outperform state-of-the-art methods. The code is available at https://github.com/hitcszx/ALFs |
| Researcher Affiliation | Academia | 1Harbin Institute of Technology 2Peng Cheng Laboratory 3King Abdullah University of Science and Technology 4Tsinghua University. |
| Pseudocode | No | No pseudocode or algorithm block found in the paper. |
| Open Source Code | Yes | The code is available at https://github.com/hitcszx/ALFs |
| Open Datasets | Yes | In this section, we empirically investigate asymmetric loss functions on benchmark datasets, including MNIST (Lecun et al., 1998), CIFAR-10/-100 (Krizhevsky & Hinton, 2009) , and a real-world noisy dataset Web Vision (Li et al., 2017). |
| Dataset Splits | No | The top-1 validation accuracies under different loss functions on the clean Web Vision validation set are reported in Table 3. |
| Hardware Specification | No | No specific hardware details (e.g., GPU models, CPU types) are provided in the paper. |
| Software Dependencies | No | The paper mentions general software components like 'deep neural networks' and 'Res Net-50' but does not specify any software names with version numbers (e.g., PyTorch 1.9) needed to replicate the experiment. |
| Experiment Setup | No | The noise generation, networks, training details, hyper-parameter settings and more experimental results can be found in the supplementary material. |