Label Distribution for Learning with Noisy Labels
Authors: Yun-Peng Liu, Ning Xu, Yu Zhang, Xin Geng
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both synthetic and real-world datasets substantiate the superiority of the proposed algorithm against state-of-the-art methods. |
| Researcher Affiliation | Academia | MOE Key Laboratory of Computer Network and Information Integration, China School of Computer Science and Engineering, Southeast University, Nanjing 210096, China {yunpengliu, xning, zhang yu, xgeng}@seu.edu.cn |
| Pseudocode | Yes | Algorithm 1 Label Distribution based Confidence Estimation |
| Open Source Code | No | The paper does not provide any statement or link regarding the open-sourcing of the described methodology's code. |
| Open Datasets | Yes | The experiments are conducted on CIFAR10 and CIFAR100 [Krizhevsky et al., 2009] with synthetic label noise and Clothing1M [Xiao et al., 2015] with real-world label noise. |
| Dataset Splits | Yes | The training set is split into two parts with the trusted fraction of 5% and 10%. Then, the synthetic label noise is added into the untrusted set. The validation set and test set have 14,313 and 10,526 images respectively. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU types, memory) used for running its experiments. |
| Software Dependencies | No | The experiments are implemented with Py Torch framework. |
| Experiment Setup | Yes | For estimation model... The learning rate is 0.1 with a deacy step 60 and a decay rate 0.1. The hyper-parameters is α=0.6, δ=0.5. ... For classifier model... The learning rate is 0.1 with a multi-step deacy [60, 80, 90] and a deacy rate 0.2. For both estimation model and classifier model, we use SGD optimizer with 0.9 momentum, a ℓ2 weight decay 1 10 4 and train the models for 100 epochs. |