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..
Mitigating Memorization of Noisy Labels by Clipping the Model Prediction
Authors: Hongxin Wei, Huiping Zhuang, Renchunzi Xie, Lei Feng, Gang Niu, Bo An, Yixuan Li
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To verify the effectiveness of our method, we conduct thorough empirical evaluations on both simulated and real-world noisy datasets, including CIFAR-10, CIFAR100 (Krizhevsky et al., 2009), and Web Vision (Li et al., 2017) datasets. |
| Researcher Affiliation | Academia | 1Southern University of Science and Technology. Work done while working at UW-Madison as a visiting scholar. 2South China University of Technology 3Nanyang Technological University 4RIKEN AIP 5University of Wisconsin-Madison. |
| Pseudocode | No | The paper does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not explicitly provide a link to its source code or state that it is open-source. |
| Open Datasets | Yes | To verify the efficacy of Logit Clip, we comprehensively consider four different types of label noise, including (1) symmetric noise, (2) asymmetric noise (Zhang & Sabuncu, 2018), (3) instance-dependent noise (Chen et al., 2020), and (4) real-world noise on CIFAR-10/100 (Krizhevsky et al., 2009) and Web Vision (Li et al., 2017) datasets. |
| Dataset Splits | Yes | We use 5k noisy samples as the validation dataset to tune the hyperparameter 1/τ in {0.1, 0.5, 1, 1.5, . . . , 4.5, 5}, then train the model on the full training set and report the average test accuracy in the last 10 epochs. |
| Hardware Specification | Yes | We conduct all the experiments on NVIDIA Ge Force RTX 3090, and implement all methods by Py Torch. |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not specify a version number or other software dependencies with their versions. |
| Experiment Setup | Yes | In particular, we train the network for 200 epochs using SGD with a momentum of 0.9, a weight decay of 0.0005, and a batch size of 128. We set the initial learning rate as 0.1, and reduce it by a factor of 10 after 80 and 140 epochs. |