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].
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). |