Event
Marc Law, University of Toronto
Tuesday, February 20, 2018 11:00to12:00
Room PCM Z240, Laboratoire d'informatique des systemes adaptatifs, CA
Shallow and Deep Metrics for Machine Learning and Computer Vision
Similarity functions and distance metrics are used in many machine learning and computer vision contexts such as clustering, k-nearest neighbors classification, support vector machine, information/image retrieval, visualization etc. Traditionally, machine learning methods fixed sample representations and the used metric before learning a model optimized for the target task. Metric learning approaches, which learn the employed metric in a supervised way, have been proposed to increase performance on tasks such as clustering. In particular, they have shown great generalization performance to compare objects from categories that were not seen during training (for instance in face verification or few-shot learning). In this talk, I will talk about different shallow and deep metric learning approaches optimized for clustering and reducing model complexity. In the clustering task, I will present efficient approaches to learn a metric in a supervised or weakly supervised way. In the model complexity context, I will present approaches to limit the rank of shallow approaches, or reduce the dimensionality of a pretrained deep neural network to perform visualization or increase zero-shot learning performance.