HASeparator: Hyperplane-Assisted Softmax
This addresses the need for more efficient feature learning in computer vision tasks, though it appears incremental as it modifies existing softmax-based approaches.
The paper tackles the problem of improving feature discrimination and intra-class compactness in CNNs by introducing HASeparator, a hyperplane-based segregation method that shows superior performance on image classification benchmarks.
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.