Learning from Noisy Labels with No Change to the Training Process
Authors: Mingyuan Zhang, Jane Lee, Shivani Agarwal
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments confirm our theoretical findings. We conducted two sets of experiments to evaluate our noise-corrected plug-in algorithm. |
| Researcher Affiliation | Collaboration | 1Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA 2Twitter, San Francisco, CA, USA (Work done while at the University of Pennsylvania). |
| Pseudocode | Yes | Algorithm 1 Noise-Corrected Plug-in Algorithm; Algorithm 3 Iterative Noise Estimation Heuristic |
| Open Source Code | No | The paper does not explicitly state that the source code for the proposed methodology is openly available or provide a link to it. |
| Open Datasets | Yes | Here we describe experiments on two benchmark data sets, MNIST (Lecun et al., 1998) and CIFAR10 (Krizhevsky & Hinton, 2009) |
| Dataset Splits | No | The paper specifies training and test set sizes for synthetic data but does not explicitly detail a separate validation split or its size/methodology. For real datasets, it states it mimicked Patrini et al.'s settings but does not provide specific split details in this paper. |
| Hardware Specification | No | No specific hardware details (like GPU or CPU models, memory, or cloud instance types) used for running experiments are provided in the paper. |
| Software Dependencies | No | The implementation was in Py Torch (Paszke et al., 2019), and used the Adam W optimizer. No version numbers for PyTorch or Adam W optimizer are provided. |
| Experiment Setup | Yes | The optimizer was run for 50 epochs over the training sample; the learning rate parameter was initially set to 0.01 and was halved at the end of every 5 epochs. |