Handling Distribution Shift in Visual Learning- Zsolt Kira скачать в хорошем качестве

Handling Distribution Shift in Visual Learning- Zsolt Kira

Abstract: While deep learning has achieved remarkable computer vision successes, fundamentally both the theory and practice for these successes have relied on vanilla supervised learning where the training and testing datasets both are sampled from the same distribution. In reality, there is likely to be a significant distribution shift once models are deployed, including noise/weather/illumination/modality changes (covariate shift), new categories (semantic shift), or different label distributions. In this talk, I will present our recent work focusing on the fundamental handling of several of these shifts. For label distribution shifts, we propose a posterior-recalibration of classifiers that can be applied without re-training to handle imbalanced datasets. For covariate and semantic shift, we propose a geometric decoupling of classifiers into feature norms and angles, showing that it can be used to learn more sensitive feature spaces for better calibration and out-of-distribution detection. We demonstrate state-of-art results across multiple benchmark datasets and metrics. In the end, I will present connections to a wider set of problems including continual/lifelong learning, open-set discovery, and semi-supervised learning. Speaker Bio: Zsolt Kira is an Assistant Professor at the Georgia Institute of Technology and Associate Director of Georgia Tech’s Machine Learning Center. His work lies at the intersection of machine learning and artificial intelligence for sensor processing, perception, and robotics. Current projects and interests relate to moving beyond the current limitations of supervised machine learning to tackle un/self-/semi-supervised methods, out-of-distribution detection, model calibration, learning under imbalance, continual/lifelong learning, and adaptation. Prof. Kira has grown a portfolio of projects funded by NSF, ONR, DARPA, and the IC community, has over 45 publications in top venues, and has received several best paper/student paper awards.