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..
Curriculum Loss: Robust Learning and Generalization against Label Corruption
Authors: Yueming Lyu, Ivor W. Tsang
ICLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on benchmark datasets validate the robustness of the proposed loss. |
| Researcher Affiliation | Academia | Yueming Lyu & Ivor W. Tsang Centre for Artificial Intelligence, University of Technology Sydney EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Partial Optimization; Algorithm 2 Training with Batch Noise Pruned Curriculum Loss |
| Open Source Code | No | The paper states, "We implement NPCL by Pytorch." However, it does not explicitly state that the source code for their proposed method (NPCL) is publicly available via a link or explicit release statement. |
| Open Datasets | Yes | We evaluate our NPCL by comparing Generalized Cross-Entropy (GCE) loss (Zhang & Sabuncu, 2018), Co-teaching (Han et al., 2018b), Co-teaching+ (Yu et al., 2019), Mentor Net (Jiang et al., 2018) and standard network training on MNIST, CIFAR10 and CIFAR100 dataset as in (Han et al., 2018b; Patrini et al., 2017; Goldberger & Ben-Reuven, 2017). |
| Dataset Splits | No | The paper mentions using MNIST, CIFAR10, and CIFAR100 datasets and evaluating on the test accuracy. However, it does not explicitly provide details about specific training/validation/test splits (e.g., percentages, sample counts, or explicit references to predefined validation splits for these datasets) beyond general mention of the datasets themselves. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, memory) used to run the experiments. |
| Software Dependencies | No | The paper states, "We implement NPCL by Pytorch." but does not provide a version number for PyTorch or any other software dependencies, which is necessary for reproducible description. |
| Experiment Setup | Yes | Specifically, the batch size and the number of epochs is set to m = 128 and N = 200, respectively. The Adam optimizer with the same parameter as (Han et al., 2018b) is employed. ... For NPCL, we employ hinge loss as the base upper bound function of 0-1 loss. In the first few epochs, we train model using full batch with soft hinge loss (in the supplement) as a burn-in period suggested in (Jiang et al., 2018). Specifically, we start NPCL at 5th epoch on MNIST and 10th epoch on CIFAR10 and CIFAR100, respectively. Appendix L provides detailed network architectures. |