Constellation Loss: Improving the efficiency of deep metric learning loss functions for optimal embedding
This work addresses efficiency improvements for metric learning in computer vision, offering incremental gains for tasks like few-shot learning and similarity measurement.
The paper tackles the slow convergence problem in deep metric learning loss functions like triplet and contrastive loss by proposing Constellation Loss, which simultaneously learns distances among all class combinations, resulting in more compact clusters and better classification results.
Metric learning has become an attractive field for research on the latest years. Loss functions like contrastive loss, triplet loss or multi-class N-pair loss have made possible generating models capable of tackling complex scenarios with the presence of many classes and scarcity on the number of images per class not only work to build classifiers, but to many other applications where measuring similarity is the key. Deep Neural Networks trained via metric learning also offer the possibility to solve few-shot learning problems. Currently used state of the art loss functions such as triplet and contrastive loss functions, still suffer from slow convergence due to the selection of effective training samples that has been partially solved by the multi-class N-pair loss by simultaneously adding additional samples from the different classes. In this work, we extend triplet and multiclass-N-pair loss function by proposing the constellation loss metric where the distances among all class combinations are simultaneously learned. We have compared our constellation loss for visual class embedding showing that our loss function over-performs the other methods by obtaining more compact clusters while achieving better classification results.