Robust Deep Graph Based Learning for Binary Classification

Understand

Convolutional neural network (CNN)-based feature learning has become state of the art, since given sufficient training data, CNN can significantly outperform traditional methods for various classification tasks.

• However, feature learning becomes more difficult if some training labels are noisy.
• With traditional regularization techniques, CNN often overfits to the noisy training labels, resulting in sub-par classification performance.
• In this paper, we propose a robust binary classifier, based on CNNs, to learn deep metric functions, which are then used to construct an optimal underlying graph structure used to clean noisy labels via graph Laplacian regularization (GLR).