Convolutional neural network (CNN)-based feature learning has become the state-of-the-art for many applications since, given sufficient training data, CNN can significantly outperform traditional methods for various classification tasks. However, feature learning is more challenging if training labels are noisy as CNN tends to overfit to the noisy training labels, resulting in sub-par classification performance. In this paper, we propose a robust binary classifier by learning CNN-based deep metric functions, to construct a graph, used to clean the noisy labels via graph Laplacian regularization (GLR). The denoised labels are then used in two proposed loss correction functions to regularize the deep metric functions. As a result, the node-to-node correlations in the graph are better reflected, leading to improved predictive performance. The experiments on three datasets, varying in number and type of features and under different levels of noise, demonstrate that given a noisy training dataset for the semi-supervised classification task, our proposed networks outperform several state-of-the-art classifiers, including label-noise robust support vector machine, CNNs with three different robust loss functions, model-based GLR, and dynamic graph CNN classifiers.

中文翻译：

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基于稳健的深度图的二进制分类学习

基于卷积神经网络（CNN）的特征学习已成为许多应用程序的最新技术，因为在给定足够的训练数据的情况下，CNN可以明显胜过传统方法进行各种分类任务。但是，如果训练标签嘈杂，则特征学习会更具挑战性，因为CNN往往会过度适应嘈杂的训练标签，从而导致分类标准表现不佳。在本文中，我们通过学习基于CNN的深度度量函数，提出了一种鲁棒的二进制分类器，以构造一个图，该图用于通过图Laplacian正则化（GLR）清除噪声标签。然后，将去噪的标签用于两个建议的损耗校正函数中，以对深度度量函数进行正则化。结果，可以更好地反映图中的节点到节点相关性，从而提高预测性能。