We employ triplet
loss as a feature embedding regularizer to boost
classification performance. Standard architectures, like
ResNet and Inception, are extended to support both losses
with minimal hyper-parameter tuning. This promotes
generality while fine-tuning pretrained networks. Triplet
loss is a powerful surrogate for recently proposed
embedding regularizers. Yet, it is avoided due to large
batch-size requirement and high computational cost.
Through our experiments, we re-assess these assumptions.
During inference, our network supports both classification
and embedding tasks without any computational overhead.
Quantitative evaluation highlights a steady improvement on
five fine-grained recognition datasets. Further evaluation
on an imbalanced video dataset achieves significant
improvement. Triplet loss brings feature embedding
characteristics like nearest neighbor to classification