Learning from Long-Tailed Noisy Data with Sample Selection and Balanced Loss
Authors: Lefan Zhang, Zhang-Hao Tian, Wujun Zhou, Wei Wang
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on benchmarks demonstrate that our method outperforms existing state-of-the-art methods. |
| Researcher Affiliation | Academia | Lefan Zhang , Zhang-Hao Tian , Wujun Zhou and Wei Wang National Key Laboratory for Novel Software Technology, Nanjing University, China School of Artificial Intelligence, Nanjing University, China {zhanglf, tianzh, zhouwujun, wangw}@lamda.nju.edu.cn |
| Pseudocode | Yes | Procedure 1 Class-Aware Sample Selection (CASS) and Algorithm 1 Learning with class-aware Sample Selection and Balanced Loss (SSBL). |
| Open Source Code | No | The paper does not provide an explicit statement or link to its open-source code for the described methodology. |
| Open Datasets | Yes | Datasets. We validate our method on seven benchmark datasets, namely CIFAR-10, CIFAR-100 [Krizhevsky et al., 2009], mini-Image Net-Red [Jiang et al., 2020], Clothing1M [Xiao et al., 2015], Food-101N [Lee et al., 2018], Animal-10N [Song et al., 2019] and Web Vision [Li et al., 2017]. |
| Dataset Splits | No | The paper does not explicitly provide specific details on validation dataset splits (e.g., percentages, sample counts, or citations to predefined validation splits). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions general software components like 'Res Net' and 'Inception-Res Net v2' as model architectures, but does not specify any programming languages, libraries, or solvers with version numbers. |
| Experiment Setup | Yes | On CIFAR-10, CIFAR-100, mini Image Net-Red and Animal-10N, we use an 18-layer Pre Act Res Net and train for 200 epochs. On Clothing1M and Food101N, we use a Res Net-50 and train for 200 epochs from scratch. On Web Vision, we use an Inception-Res Net v2 and train for 100 epochs following [Li et al., 2020]. On all datasets, γsup is set as 3 and γrel is set as 1 in L L (refer to Appendix A for more details). |