  Despite significant recent advances in similarity detection tasks, existing
approaches pose substantial challenges under memory constraints. One of the
primary reasons for this is the use of computationally expensive metric
learning loss functions such as Triplet Loss in Siamese networks. In this
paper, we present a novel loss function called Shadow Loss that compresses the
dimensions of an embedding space during loss calculation without loss of
performance. The distance between the projections of the embeddings is learned
from inputs on a compact projection space where distances directly correspond
to a measure of class similarity. Projecting on a lower-dimension projection
space, our loss function converges faster, and the resulting classified image
clusters have higher inter-class and smaller intra-class distances. Shadow Loss
not only reduces embedding dimensions favoring memory constraint devices but
also consistently performs better than the state-of-the-art Triplet Margin Loss
by an accuracy of 5\%-10\% across diverse datasets. The proposed loss function
is also model agnostic, upholding its performance across several tested models.
Its effectiveness and robustness across balanced, imbalanced, medical, and
non-medical image datasets suggests that it is not specific to a particular
model or dataset but demonstrates superior performance consistently while using
less memory and computation.
