Curriculum Loss: Robust Learning and Generalization against Label Corruption
Authors: Yueming Lyu, Ivor W. Tsang
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | 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 yueminglyu@gmail.com, Ivor.Tsang@uts.edu.au |
| 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. |