Noisy labels are very common in real-world training data, which lead to poor generalization on test data because of overfitting to the noisy labels. In this paper, we claim that such overfitting can be avoided by “early stopping” training a deep …
Finding low-dimensional embeddings of sparse high-dimensional data objects is important in many applications such as recommendation, graph mining, and natural language processing (NLP). Recently, autoencoder (AE)-based embedding approaches have …
Owing to the extremely high expressive power of deep neural networks, their side effect is to totally memorize training data even when the labels are extremely noisy. To overcome overfitting on the noisy labels, we propose a novel robust training …