FINE Samples for Learning with Noisy Labels
Authors: Taehyeon Kim, Jongwoo Ko, sangwook Cho, JinHwan Choi, Se-Young Yun
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that the proposed methods consistently outperform corresponding baselines for all three applications on various benchmark datasets. 1 Introduction Deep neural networks (DNNs) have achieved remarkable success in numerous tasks as the amount of accessible data has dramatically increased [21, 15]. |
| Researcher Affiliation | Academia | Taehyeon Kim KAIST AI KAIST Daejeon, South Korea potter32@kaist.ac.kr Jongwoo Ko KAIST AI KAIST Daejeon, South Korea jongwoo.ko@kaist.ac.kr Sangwook Cho KAIST AI KAIST Daejeon, South Korea sangwookcho@kaist.ac.kr Jinhwan Choi KAIST AI KAIST Daejeon, South Korea jinhwanchoi@kaist.ac.kr Se-Young Yun KAIST AI KAIST Daejeon, South Korea yunseyoung@kaist.ac.kr |
| Pseudocode | Yes | Algorithm 1: FINE Algorithm for Sample Selection |
| Open Source Code | Yes | Code available at https://github.com/Kthyeon/FINE_official |
| Open Datasets | Yes | Noisy Benchmark Dataset. Following the previous setups [25, 29], we artificially generate two types of random noisy labels: injecting uniform randomness into a fraction of labels (symmetric) and corrupting a label only to a specific class (asymmetric). For example, we generate noise by mapping TRUCK AUTOMOBILE, BIRD AIRPLANE, DEER HORSE, CAT DOG to make asymmetric noise for CIFAR-10. For CIFAR-100, we create 20 five-size super-classes and generate asymmetric noise by changing each class to the next class within super-classes circularly. For a real-world dataset, Clothing1M [44] containing inherent noisy labels is used. |
| Dataset Splits | Yes | The dataset provides 50k, 14k, and 10k verified as clean data for training, validation, and testing. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) are mentioned for the experiments. |
| Software Dependencies | No | No specific ancillary software details with version numbers (e.g., PyTorch 1.9, Python 3.8) are provided. |
| Experiment Setup | Yes | We set the threshold ζ as 0.5. |