Consistency-Based Semi-Supervised Learning for Object Detection

This course covers the learning outcomes and goals of consistency-based semi-supervised learning for object detection. It teaches the understanding of the problem and terminologies related to object detection, motivation for semi-supervised learning, consistency regularization, loss functions, and background elimination. The course also includes discussions on experiments, results, limitations, and conclusions. The individual skills or tools taught include implementing consistency-based semi-supervised learning techniques for object detection. The teaching method involves a presentation format with slides. The intended audience for this course is individuals interested in computer vision, object detection, and semi-supervised learning techniques.