During training of deep neural networks, a Copernican loss (LC) is designed to augment a primary loss function, for example, a standard Softmax loss, to explicitly minimize intra-class variation and simultaneously maximize inter-class variation. Copernican loss operates using the cosine distance and thereby affects angles leading to a cosine embedding, which removes the disconnect between training and testing.