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..
DivideMix: Learning with Noisy Labels as Semi-supervised Learning
Authors: Junnan Li, Richard Socher, Steven C.H. Hoi
ICLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on multiple benchmark datasets demonstrate substantial improvements over state-of-the-art methods. |
| Researcher Affiliation | Industry | Junnan Li, Richard Socher, Steven C.H. Hoi Salesforce Research EMAIL |
| Pseudocode | Yes | Algorithm 1: Divide Mix. |
| Open Source Code | Yes | Code is available at https://github.com/Li Junnan1992/Divide Mix. |
| Open Datasets | Yes | We extensively validate our method on four benchmark datasets, namely CIFAR-10, CIFAR100 (Krizhevsky & Hinton, 2009), Clothing1M (Xiao et al., 2015), and Web Vision (Li et al., 2017a). |
| Dataset Splits | Yes | CIFAR-10 and CIFAR-100 contain 50K training images and 10K test images of size 32 32. We choose λu from {0, 25, 50, 150} using a small validation set. |
| Hardware Specification | Yes | In Table 8, we compare the total training time of Divide Mix on CIFAR-10 with several state-of-the-art methods, using a single Nvidia V100 GPU. |
| Software Dependencies | No | The paper describes the neural network architecture (18-layer Pre Act Resnet) and training optimizers (SGD) along with their parameters, but it does not specify versions for any programming languages or libraries (e.g., Python, PyTorch, TensorFlow). |
| Experiment Setup | Yes | We use an 18-layer Pre Act Resnet (He et al., 2016) and train it using SGD with a momentum of 0.9, a weight decay of 0.0005, and a batch size of 128. The network is trained for 300 epochs. We set the initial learning rate as 0.02, and reduce it by a factor of 10 after 150 epochs. The warm up period is 10 epochs for CIFAR-10 and 30 epochs for CIFAR-100. We find that most hyperparameters introduced by Divide Mix do not need to be heavily tuned. For all CIFAR experiments, we use the same hyperparameters M = 2, T = 0.5, and α = 4. τ is set as 0.5 except for 90% noise ratio when it is set as 0.6. |