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 …

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 …

Abstract We present Ego-Exo4D a diverse large-scale multimodal multiview video dataset
and benchmark challenge. Ego-Exo4D centers around simultaneously-captured egocentric …

Widely used alignment techniques, such as reinforcement learning from human feedback
(RLHF), rely on the ability of humans to supervise model behavior-for example, to evaluate …

Data-centric AI is at the center of a fundamental shift in software engineering where machine
learning becomes the new software, powered by big data and computing infrastructure …

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 …

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 …

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 Learning with noisy labels is a practically challenging problem in weakly-supervised
learning. The state-of-the-art approaches" Decoupling" and" Co-teaching+" claim that the" …

Recent advances in domain adaptation show that deep self-training presents a powerful
means for unsupervised domain adaptation. These methods often involve an iterative …