Learning Incoherent Subspaces: Classification via Incoherent Dictionary Learning

In this article we present the supervised iterative projections and rotations (s-ipr) algorithm, a method for learning discriminative incoherent subspaces from data. We derive s-ipr as a supervised extension of our previously proposed iterative projections and rotations (ipr) algorithm for incoherent dictionary learning, and we employ it to learn incoherent sub-spaces that model signals belonging to different classes. We test our method as a feature transform for supervised classification, first by visualising transformed features from a synthetic dataset and from the ‘iris’ dataset, then by using the resulting features in a classification experiment

Learning incoherent dictionaries for sparse approximation using iterative projections and rotations

This work was supported by the Queen Mary University of London School Studentship, the EU FET-Open project FP7- ICT-225913-SMALL. Sparse Models, Algorithms and Learning for Large-scale data and a Leadership Fellowship from the UK Engineering and Physical Sciences Research Council (EPSRC)

Spherical Regression: Learning Viewpoints, Surface Normals and 3D Rotations on n-Spheres

Many computer vision challenges require continuous outputs, but tend to be solved by discrete classification. The reason is classification's natural containment within a probability $n$-simplex, as defined by the popular softmax activation function. Regular regression lacks such a closed geometry, leading to unstable training and convergence to suboptimal local minima. Starting from this insight we revisit regression in convolutional neural networks. We observe many continuous output problems in computer vision are naturally contained in closed geometrical manifolds, like the Euler angles in viewpoint estimation or the normals in surface normal estimation. A natural framework for posing such continuous output problems are $n$-spheres, which are naturally closed geometric manifolds defined in the $\mathbb{R}^{(n+1)}$ space. By introducing a spherical exponential mapping on $n$-spheres at the regression output, we obtain well-behaved gradients, leading to stable training. We show how our spherical regression can be utilized for several computer vision challenges, specifically viewpoint estimation, surface normal estimation and 3D rotation estimation. For all these problems our experiments demonstrate the benefit of spherical regression. All paper resources are available at https://github.com/leoshine/Spherical_Regression.Comment: CVPR 2019 camera read

Dental Service-Learning Curriculum and Community Outreach Programs Perception vs. Practice

The purpose of this study is to determine if the service-learning aspect of Marquette’s dental education enhances the dental students’ knowledge of the barriers to access to dental care for underserved populations. The goal of this study is to obtain feedback about service-learning in the MUSoD curriculum and disseminate the findings to others who teach service-learning in dental school curricula. MUSoD students have the opportunity to participate in multiple diverse outreach experiences throughout their four years of Dental School. Their attitudes toward service experiences and their perception of service-learning curriculum before and after they perform rotations will be recorded. Volunteers will be recruited with posters strategically placed throughout the school. Each class will also be contacted by email. Seven dental students from each class, D1, D2, D3 and D4 will be randomly selected to attend a one hour focus group during their lunch hour to discuss the MUSoD service-learning curriculum