Deep learning has achieved remarkable success in numerous domains with help from large
amounts of big data. However, the quality of data labels is a concern because of the lack of …

Deep semi-supervised learning is a fast-growing field with a range of practical applications.
This paper provides a comprehensive survey on both fundamentals and recent advances in …

Deep neural networks (DNNs) have achieved tremendous success in a variety of
applications across many disciplines. Yet, their superior performance comes with the …

Deep learning with noisy labels is practically challenging, as the capacity of deep models is
so high that they can totally memorize these noisy labels sooner or later during training …

Curriculum learning (CL) is a training strategy that trains a machine learning model from
easier data to harder data, which imitates the meaningful learning order in human curricula …

In this paper, we present a simple and efficient method for training deep neural networks in a
semi-supervised setting where only a small portion of training data is labeled. We introduce …

Deep neural networks are known to be annotation-hungry. Numerous efforts have been
devoted to reducing the annotation cost when learning with deep networks. Two prominent …

Deep neural networks have been shown to be very powerful modeling tools for many
supervised learning tasks involving complex input patterns. However, they can also easily …

Recent deep networks are capable of memorizing the entire data even when the labels are
completely random. To overcome the overfitting on corrupted labels, we propose a novel …

We propose a novel framework to perform classification via deep learning in the presence of
noisy annotations. When trained on noisy labels, deep neural networks have been observed …