  Discriminative features are critical for machine learning applications. Most
existing deep learning approaches, however, rely on convolutional neural
networks (CNNs) for learning features, whose discriminant power is not
explicitly enforced. In this paper, we propose a novel approach to train deep
CNNs by imposing the intra-class compactness and the inter-class separability,
so as to enhance the learned features' discriminant power. To this end, we
introduce anchors, which are predefined vectors regarded as the centers for
each class and fixed during training. Discriminative features are obtained by
constraining the deep CNNs to map training samples to the corresponding anchors
as close as possible. We propose two principles to select the anchors, and
measure the proximity of two points using the Euclidean and cosine distance
metric functions, which results in two novel loss functions. These loss
functions require no sample pairs or triplets and can be efficiently optimized
by batch stochastic gradient descent. We test the proposed method on three
benchmark image classification datasets and demonstrate its promising results.
