Related papers: Learning from Long-Tailed Noisy Data with Sample Selection and Balanced Loss

Learning from Long-Tailed Noisy Data with Sample Selection and Balanced Loss

URL: http://arxiv.org/abs/2211.10906v3
Date: Sun, 28 May 2023 08:39:46 GMT
Title: Learning from Long-Tailed Noisy Data with Sample Selection and Balanced Loss
Authors: Lefan Zhang, Zhang-Hao Tian, Wujun Zhou, Wei Wang
Abstract summary: 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.
Score: 8.71234615872208
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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