EAGER: Large-Scale Distributed Learning of Noisy Labels for Images and Video

Wayne State University, Detroit MI

Investigators

Abstract

This project develops algorithms for learning from images and video with noisy labels. The overwhelming amounts of images and video freely available online present unprecedented challenges for machine learning and computer vision research communities. They also bring tremendous opportunities and great potentials for addressing human-machine semantic gaps in image understanding and for revolutionizing our ways to index, retrieve, and interact with images and video. Inaccurate labels and mislabeled data are common problems for image and video datasets. Noisy labels would cause problems with the existing learning algorithms. This project can have broad impacts on other big data problem. The project is integrated with education by training students, ensuring broad participation of underrepresented groups, and outreaching general public. This research exploring distributed learning methods for large-scale images and video with noisy labels. The PI investigates the learning problem of loss functions with both smooth and non-smooth regularization terms, and accordingly develops new distributed learning algorithms that are capable of leveraging the abundance of images that are too large to fit into a single machine. The research has an immense potential in image and video analysis, and computer vision applications. Specifically, this research emphasizes both algorithmic and theoretic aspects by (1) developing distributed learning based approaches for optimization and learning of noisy labels; and (2) investigating issues such as guaranteed convergence, convergence rate, and scalability. This work provides new methods that are widely applicable to many economically, medically and scientifically important large-scale datasets for novel discoveries across many domains.