Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model
Authors: Qizhou Wang, Bo Han, Tongliang Liu, Gang Niu, Jian Yang, Chen Gong10183-10191
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on datasets with both synthetic and real-world label noise verify that the proposed method yields significant improvements on robustness over state-of-the-art counterparts. |
| Researcher Affiliation | Academia | 1 Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Mo E, School of Computer Science and Engineering, Nanjing University of Science and Technology 2 Department of Computer Science, Hong Kong Baptist University 3 Trustworthy Machine Learning Lab, School of Computer Science, Faculty of Engineering, The University of Sydney 4 RIKEN Center for Advanced Intelligence Project (AIP) 5 Department of Computing, Hong Kong Polytechnic University |
| Pseudocode | Yes | Algorithm 1 The Overall Algorithm. |
| Open Source Code | No | The paper does not provide any link to open-source code or explicitly state that code is available. |
| Open Datasets | Yes | We first test the proposed method on CIFAR-100 and CIFAR10 (Krizhevsky, Hinton et al. 2009) with synthetic label noise, and then conduct real-world label noise experiments on Clothing1M (Xiao et al. 2015). |
| Dataset Splits | Yes | Clothing1M: ...it also contains 50k, 14k, and 10k correctly labeled instances for auxiliary training, validation, and test. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions models like 'Res Net-32' and 'Res Net-50' but does not provide specific software dependencies with version numbers (e.g., Python, TensorFlow, PyTorch versions). |
| Experiment Setup | Yes | Meansubtraction, horizontal random flip, and 32 32 random crop are performed for data pre-processing. Then, we use minibatch gradient ascent with a momentum of 0.9; a weight decay of 10 4; and a batch size of 256. The alternating optimization algorithm is executed for 160 epochs. For the classifier parameters, we begin with a learning rate α2 = 0.05 and divide it by 10 per 40 epochs. For label confusing probabilities, they are updated every 5 epochs from the 35th epoch. We test model performance with different learning rate α1 on CIFAR-100, and fix α1 = 0.7 on CIFAR-10. |