Learning from Long-Tailed Noisy Data with Sample Selection and Balanced Loss
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Abstract (arXiv)
The success of deep learning depends on large-scale and well-curated training data, while data in real-world applications are commonly long-tailed and noisy. Many methods have been proposed to deal with long-tailed data or noisy data, while a few methods are developed to tackle long-tailed noisy data. To solve this, we propose a robust method for learning from long-tailed noisy data with sample selection and balanced loss. Specifically, we separate the noisy training data into clean labeled set and unlabeled set with sample selection, and train the deep neural network in a semi-supervised manner with a balanced loss based on model bias. Extensive experiments on benchmarks demonstrate that our method outperforms existing state-of-the-art methods.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 51 of its 72 references that are in the map. First posted on arXiv 20 Nov 2022.
Builds on
51 of 72 references.
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