This paper introduces a novel variant of the Triplet Loss function that converges faster and gives better results. This function can separate class instances homogeneously through the whole embedding space. With Exponential Triplet Loss function we also introduce a novel type of embedding space regularization Unit-Range and Unit-Bounce that utilizes euclidean space more efficiently and resembles features of the cosine distance. We also examined factors for choosing the best embedding vector size for specific embedding spaces. Finally, we also demonstrate how new function can train models for one-shot learning and re-identification tasks.
Data-sets used:
- VGGFace-2
- CIFAR-10
- Fassion-MNIST
