  Deep neural networks have achieved remarkable performance on a range of
classification tasks, with softmax cross-entropy (CE) loss emerging as the
de-facto objective function. The CE loss encourages features of a class to have
a higher projection score on the true class-vector compared to the negative
classes. However, this is a relative constraint and does not explicitly force
different class features to be well-separated. Motivated by the observation
that ground-truth class representations in CE loss are orthogonal (one-hot
encoded vectors), we develop a novel loss function termed `Orthogonal
Projection Loss' (OPL) which imposes orthogonality in the feature space. OPL
augments the properties of CE loss and directly enforces inter-class separation
alongside intra-class clustering in the feature space through orthogonality
constraints on the mini-batch level. As compared to other alternatives of CE,
OPL offers unique advantages e.g., no additional learnable parameters, does not
require careful negative mining and is not sensitive to the batch size. Given
the plug-and-play nature of OPL, we evaluate it on a diverse range of tasks
including image recognition (CIFAR-100), large-scale classification (ImageNet),
domain generalization (PACS) and few-shot learning (miniImageNet, CIFAR-FS,
tiered-ImageNet and Meta-dataset) and demonstrate its effectiveness across the
board. Furthermore, OPL offers better robustness against practical nuisances
such as adversarial attacks and label noise. Code is available at:
https://github.com/kahnchana/opl.
