Ranked List Loss for Deep Metric Learning.

Area of Science:

• Computer Science
• Machine Learning
• Artificial Intelligence

Background:

• Deep metric learning (DML) aims to learn data embeddings capturing semantic relationships.
• Existing loss functions (pairwise, tripletwise) suffer from slow convergence due to trivial examples.
• Ranking-motivated structured losses offer faster convergence but have limitations.

Purpose of the Study:

• To address limitations in existing ranking-motivated structured losses for deep metric learning.
• To propose a novel ranked list loss that improves convergence and embedding quality.
• To enhance the informativeness of similarity structures and preserve intraclass data distribution.

Main Methods:

• Developed a set-based similarity structure utilizing all gallery instances for richer information.
• Interpreted the learning as few-shot retrieval, iteratively using each example as a query.
• Introduced hypersphere learning per class to maintain intraclass structure and act as regularization.

Main Results:

• The proposed ranked list loss significantly improves convergence speed compared to existing methods.
• Experimental results demonstrate superior performance on fine-grained image retrieval tasks.
• The method effectively preserves intraclass similarity structure, avoiding over-compression.

Conclusions:

• The novel ranked list loss offers a superior approach to deep metric learning.
• The method enhances learning by incorporating comprehensive similarity structures and preserving intraclass diversity.
• This work advances the state-of-the-art in deep metric learning and image retrieval.