论文标题
使用标签噪声选择的嘈杂标签分类，用于测试时间增强跨凝结和Noisemix学习
Noisy Label Classification using Label Noise Selection with Test-Time Augmentation Cross-Entropy and NoiseMix Learning
论文作者
论文摘要
随着深度学习任务中使用的数据集的大小增加，嘈杂的标签问题是使深度学习对错误标记的数据的鲁棒性的任务，已成为一项重要任务。在本文中，我们提出了一种使用Noisemix方法的测试时间增强（TTA）跨透明层和分类器学习的标签噪声数据学习噪声标签数据的方法。在标签噪声选择中，我们通过测量跨凝性预测测试时间增强训练数据来提出TTA跨透镜。在分类器学习中，我们通过混合来自嘈杂和干净的标签数据的样品来提出基于混合和平衡混合方法的Noisemix方法。在ISIC-18公共皮肤病变诊断数据集的实验中，拟议的TTA跨透明镜在检测标签噪声选择过程中检测标签噪声数据时的传统跨透镜和TTA不确定性。此外，提议的Noisemix不仅表现出分类性能中最新的方法，而且还表现出对分类器学习中标签噪声的最强性。
As the size of the dataset used in deep learning tasks increases, the noisy label problem, which is a task of making deep learning robust to the incorrectly labeled data, has become an important task. In this paper, we propose a method of learning noisy label data using the label noise selection with test-time augmentation (TTA) cross-entropy and classifier learning with the NoiseMix method. In the label noise selection, we propose TTA cross-entropy by measuring the cross-entropy to predict the test-time augmented training data. In the classifier learning, we propose the NoiseMix method based on MixUp and BalancedMix methods by mixing the samples from the noisy and the clean label data. In experiments on the ISIC-18 public skin lesion diagnosis dataset, the proposed TTA cross-entropy outperformed the conventional cross-entropy and the TTA uncertainty in detecting label noise data in the label noise selection process. Moreover, the proposed NoiseMix not only outperformed the state-of-the-art methods in the classification performance but also showed the most robustness to the label noise in the classifier learning.
