Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise
Authors: Pengfei Chen, Junjie Ye, Guangyong Chen, Jingwei Zhao, Pheng-Ann Heng11442-11450
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on MNIST and CIFAR-10 under IDN with varying noise fractions generated by Algorithm 1. The superior performance of SEAL is verified on extensive experiments, including synthetic/real-world datasets under IDN of different noise fractions, and the large benchmark Clothing1M. |
| Researcher Affiliation | Collaboration | 1 The Chinese University of Hong Kong 2 VIVO AI Lab 3 Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences |
| Pseudocode | Yes | Algorithm 1 IDN Generation. Algorithm 2 An iteration of SEAL. |
| Open Source Code | Yes | Our code is released1. 1https://github.com/chenpf1025/IDN |
| Open Datasets | Yes | We conduct experiments on MNIST and CIFAR-10 (Krizhevsky and Hinton 2009) with varying IDN fractions as well as large-scale real-world noise benchmark Clothing1M (Xiao et al. 2015). |
| Dataset Splits | Yes | The training set consists of 1M noisy instances and the additional validation, testing sets consist of 14K, 10K clean instances. We keep 500k random samples from validation while train a Res Net-50 on the rest samples. |
| Hardware Specification | No | The paper mentions training models but does not provide specific details on the hardware used, such as GPU/CPU models or memory specifications. |
| Software Dependencies | No | The paper specifies optimizers and hyperparameters (e.g., 'SGD with a momentum of 0.5'), but it does not list specific software libraries or frameworks with their version numbers (e.g., PyTorch 1.9, TensorFlow 2.x). |
| Experiment Setup | Yes | On MNIST, ...Models are trained for 50 epochs with a batch size of 64 and we report the testing accuracy at the last epoch. For the optimizer, we use SGD with a momentum of 0.5, a learning rete of 0.01, without weight decay. On CIFAR-10, ...Models are trained for 150 epochs with a batch size of 128... For the optimizer, we use SGD with a momentum of 0.9 and a weight decay of 5 10 4. The learning rate is initialized as 0.1 and is divided by 5 after 60 and 120 epochs. ...On Clothing1M, ...Models are trained for 10 epochs with a batch size of 256... For the optimizer, we use SGD with a momentum of 0.9 and a weight decay of 10 3. We use a learning rate of 10 3 in the first 5 epochs and 10 4 in the second 5 epochs... |