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..
Label Distribution for Learning with Noisy Labels
Authors: Yun-Peng Liu, Ning Xu, Yu Zhang, Xin Geng
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both synthetic and real-world datasets substantiate the superiority of the proposed algorithm against state-of-the-art methods. |
| Researcher Affiliation | Academia | MOE Key Laboratory of Computer Network and Information Integration, China School of Computer Science and Engineering, Southeast University, Nanjing 210096, China EMAIL |
| Pseudocode | Yes | Algorithm 1 Label Distribution based Confidence Estimation |
| Open Source Code | No | The paper does not provide any statement or link regarding the open-sourcing of the described methodology's code. |
| Open Datasets | Yes | The experiments are conducted on CIFAR10 and CIFAR100 [Krizhevsky et al., 2009] with synthetic label noise and Clothing1M [Xiao et al., 2015] with real-world label noise. |
| Dataset Splits | Yes | The training set is split into two parts with the trusted fraction of 5% and 10%. Then, the synthetic label noise is added into the untrusted set. The validation set and test set have 14,313 and 10,526 images respectively. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU types, memory) used for running its experiments. |
| Software Dependencies | No | The experiments are implemented with Py Torch framework. |
| Experiment Setup | Yes | For estimation model... The learning rate is 0.1 with a deacy step 60 and a decay rate 0.1. The hyper-parameters is α=0.6, δ=0.5. ... For classifier model... The learning rate is 0.1 with a multi-step deacy [60, 80, 90] and a deacy rate 0.2. For both estimation model and classifier model, we use SGD optimizer with 0.9 momentum, a ℓ2 weight decay 1 10 4 and train the models for 100 epochs. |