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..
Mutual Partial Label Learning with Competitive Label Noise
Authors: Yan Yan, Yuhong Guo
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted on several benchmark PLL datasets, and the proposed ML-PLL approach demonstrates state-of-the-art performance for partial label learning. |
| Researcher Affiliation | Academia | Yan Yan1, Yuhong Guo1,2 1Carleton University, Ottawa, Canada 2CIFAR AI Chair, Amii, Canada EMAIL, EMAIL |
| Pseudocode | Yes | We present the mini-batch based training algorithm for ML-PLL in Algorithm 1. Algorithm 1 Training Algorithm for ML-PLL. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | We conducted experiments on four widely used benchmark image datasets: Fashion MNIST (Xiao et al., 2017), Kuzushiji-MNIST (Clanuwat et al., 2018), CIFAR-10 and CIFAR-100 (Krizhevsky et al., 2009). |
| Dataset Splits | Yes | The parameter α and β are chosen from {0.5, 0.6, 0.7, 0.8, 0.9, 1} and {1, 2, 3, 4, 5, 6}, respectively, according to the accuracy on a validation dataset (10% of the training dataset). |
| Hardware Specification | No | The paper does not specify the hardware used for experiments, such as CPU or GPU models, or cloud computing resources. |
| Software Dependencies | No | The paper mentions using a "standard SGD optimizer" but does not provide specific version numbers for any software dependencies, libraries, or programming languages. |
| Experiment Setup | Yes | The weighted combination parameter λ in Eq.(1), the temperature parameter τ in Eq.(2), and the momentum coefficients in Eq.(5) and Eq.(9) are set to 0.99, 1, 0.999, and 0.99 respectively. In all the experiments, we utilize a standard SGD optimizer with a momentum of 0.9 and a weight decay of 1e-3 for model training. The mini-batch size, learning rate and total training epochs are set to 128, 0.01 and 400 respectively. The parameter α and β are chosen from {0.5, 0.6, 0.7, 0.8, 0.9, 1} and {1, 2, 3, 4, 5, 6}, respectively, according to the accuracy on a validation dataset (10% of the training dataset). |