  Efficient feature learning with Convolutional Neural Networks (CNNs)
constitutes an increasingly imperative property since several challenging tasks
of computer vision tend to require cascade schemes and modalities fusion.
Feature learning aims at CNN models capable of extracting embeddings,
exhibiting high discrimination among the different classes, as well as
intra-class compactness. In this paper, a novel approach is introduced that has
separator, which focuses on an effective hyperplane-based segregation of the
classes instead of the common class centers separation scheme. Accordingly, an
innovatory separator, namely the Hyperplane-Assisted Softmax separator
(HASeparator), is proposed that demonstrates superior discrimination
capabilities, as evaluated on popular image classification benchmarks.
