​​Triplet loss - Advanced Intro Loss functions in metric lear | Neural Networks Engineering

​​Triplet loss - Advanced Intro

Loss functions in metric learning are all chasing the same goal - to make positive pairs closer and negative further.
But the way they achieve this leads to different results and different side effects.

In today's post, we describe the differences between Triplet and Contrastive loss, why the use of Triplet loss can give an advantage, especially in the context of fine-tuning.
It also covers the approach to an efficient implementation of batch-all triplet mining.