You are invited to attend our next UW CPAMI Graduate Seminar, which will include two presentations.
Yun-Qian Miao will speak on “Discriminative Density Ratio for Classification Domain Adaptation”.
Samaneh Hosseini Semnani will speak on “Multi-Target Tracking in Large-Scale Surveillance Systems”.
University of Waterloo
Engineering 5
Room E5-5106
Refreshments will be provided
Please see seminar notices below for more information:
Multi-Target Tracking in Large-Scale Surveillance Systems
Abstract :
Sensor Networks is an emerging field of study that is expected to touch many aspects of our life. Sensor Networks are well studied and efficiently used in mobile surveillance applications. One key challenge in large-scale surveillance systems is the mobility control and coordination of sensors. One of the most powerful algorithms that has attracted many researchers in the field of mobility control in recent years is the Flocking algorithm . Flocking is a form of cooperative behavior of large number of autonomous interacting agents able to show coordinated group behavior. The self-organizing feature and local communication requirement of flocks can provide deeper insight into the management of sensors in sensor networks.
Discriminative Density Ratio for Classification Domain Adaptation
Abstract :
Domain adaptation deals with the challenging problem that has arisen from the distribution difference between the training and the test data. An effective approach is to reweight the training samples to minimize that discrepancy. To be specific, many methods are based on developing Density-ratio (DR) estimation techniques and then applying cost-sensitive learning algorithms to the reweighted data. Although these methods work well for regression problems, their performance on classification problems is unsatisfactory. This is due to the fact that these methods focus on matching the sample marginal distributions without paying attention to preserving the separation between classes in the reweighted space. In this paper, we propose a novel method for Discriminative Density-ratio (DDR) estimation that addresses the aforementioned problem by estimating the density ratio of joint distributions in a class-wise manner. Our approach combines three learning objectives: minimizing generalized risk on the
reweighted training data, minimizing class-wise distribution discrepancy, and maximizing the separation margin on the test data. To solve the complex DDR problem, two implementations are presented on the basis of block-coordinate update method. We provide analysis on the theoretical convergence of the presented implementations and demonstrate their effectiveness with extensive experiments conducted on sampling bias scenarios and cross-dataset tasks.