Machine Learning and Information Theory
The recent successes in image and video analysis have been largely in the domain of supervised learning.Supervised learning methods assume the availability of extensive amounts of manually annotated/labeled training data, which limits the applicability of existing methods to complex and unseen environments. This has motivated growing interest in developing semi-supervised, and even unsupervised, methods for image and video analysis, i.e., methods that have limited or even no manually annotated data. These methods focus on how to learn visual analysis models with limited labeled data; however, the problem of what to label is far less addressed. If one can identify the optimal subset to label, it is likely that the learning process will be more efficient than randomly choosing representatives that are labeled by a human. This project will focus on mathematically rigorous approaches on how to choose these samples to label.
Sk. M. Ahmed*, A. R. lejbolle*, R. Panda, A. Roy-Chowdhury, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2020. (*joint first authors)
J. Bappy, S. Paul, E. Tuncel, A. Roy-Chowdhury, IEEE Trans. on Image Processing (T-IP), 2019.