Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels
Authors: Pengfei Chen, Ben Ben Liao, Guangyong Chen, Shengyu Zhang
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both synthetic and real-world noisy labels show that compared with extensive state-of-the-art methods, our strategy consistently improves the generalization performance of DNNs under both synthetic and real-world training noise. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science and Engineering, The Chinese University of Hong Kong 2Tencent Technology. Correspondence to: Guangyong Chen <gycchen@tencent.com>. |
| Pseudocode | Yes | Algorithm 1 Noisy Cross-Validation (NCV): selecting clean samples out of the noisy ones; Algorithm 2 Iterative Noisy Cross-Validation (INCV): selecting clean samples out of the noisy ones; Algorithm 3 Training DNNs robustly against noisy labels |
| Open Source Code | Yes | Our code is available at https://github.com/chenpf1025/ noisy_label_understanding_utilizing. |
| Open Datasets | Yes | Our method is verified on (i) the CIFAR-10 dataset (Krizhevsky & Hinton, 2009) with synthetic noisy labels generated by randomly flipping the original ones, and (ii) the Web Vision dataset (Li et al., 2017), which is a large benchmark consisting of 2.4 million images crawled from websites, containing real-world noisy labels. |
| Dataset Splits | Yes | Given a noisy dataset D, we implement cross-validation to randomly split it into two halves D1, D2, then train the Res Net-110 (He et al., 2016b) on D1 and test on D2. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as GPU models, CPU types, or memory. |
| Software Dependencies | No | The paper mentions that the implementation is based on PyTorch but does not specify a version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | In the experiments, we set the batch size |BS| to 128, then compute |BC| accordingly. ... we set n(e) = |BS|(1 εS min(e/10, 1)), which means we decrease n(e) from |BS| to |BS|(1 εS) linearly at the first 10 epochs and fix it after that. |