Constellation Loss: Improving The Efficiency Of Deep Metric Learning Loss Functions For Optimal Embedding

Abstract

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 comb

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keymedela2019constellation

Related papers

• The Group Loss For Deep Metric Learning (2019)11.39
• Shadow Loss: Memory-linear Deep Metric Learning For Efficient Training (2023)0.00
• Ensemble Of Loss Functions To Improve Generalizability Of Deep Metric Learning Methods (2021)7.16
• The Group Loss++: A Deeper Look Into Group Loss For Deep Metric Learning (2022)8.82
• Ranked List Loss For Deep Metric Learning (2019)7.50
• Anti-collapse Loss For Deep Metric Learning Based On Coding Rate Metric (2024)0.00
• Rethinking Preventing Class-collapsing In Metric Learning With Margin-based Losses (2020)9.23
• Variance & Greediness: A Comparative Study Of Metric-learning Losses (2026)0.00