Bag Of Negatives For Siamese Architectures
Bojana Gajic, Ariel Amato, Ramon Baldrich, Carlo Gatta . Arxiv 2019 – 2 citations
[Paper]
Training a Siamese architecture for re-identification with a large number of identities is a challenging task due to the difficulty of finding relevant negative samples efficiently. In this work we present Bag of Negatives (BoN), a method for accelerated and improved training of Siamese networks that scales well on datasets with a very large number of identities. BoN is an efficient and loss-independent method, able to select a bag of high quality negatives, based on a novel online hashing strategy.