Reaching Nirvana: Maximizing the Margin in Both Euclidean and Angular Spaces for Deep Neural Network Classification.

The classification loss functions used in deep neural network classifiers can be split into two categories based on maximizing the margin in either Euclidean or angular spaces. Euclidean distances between sample vectors are used during classification for the methods maximizing the margin in Euclidean spaces whereas the Cosine similarity distance is used during the testing stage for the methods maximizing the margin in the angular spaces. This article introduces a novel classification loss that maximizes the margin in both the Euclidean and angular spaces at the same time.

Deep Discriminative Feature Models (DDFMs) for Set Based Face Recognition and Distance Metric Learning.

This article introduces two methods that find compact deep feature models for approximating images in set based face recognition problems. The proposed method treats each image set as a nonlinear face manifold that is composed of linear components. To find linear components of the face manifold, we first split image sets into subsets containing face images which share similar appearances.