  In this work, we propose a new loss to improve feature discriminability and
classification performance. Motivated by the adaptive cosine/coherence
estimator (ACE), our proposed method incorporates angular information that is
inherently learned by artificial neural networks. Our learnable ACE (LACE)
transforms the data into a new "whitened" space that improves the inter-class
separability and intra-class compactness. We compare our LACE to alternative
state-of-the art softmax-based and feature regularization approaches. Our
results show that the proposed method can serve as a viable alternative to
cross entropy and angular softmax approaches. Our code is publicly available:
https://github.com/GatorSense/LACE.
