A New Framework of Collaborative Learning for Adaptive Metric Distillation

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Traditional knowledge distillation (KD) methods often overlook inter-sample relationships in feature spaces.
• This limitation significantly impacts performance, particularly in retrieval tasks.
• Existing KD approaches primarily transfer knowledge via classifier logits or feature structures.

Purpose of the Study:

• To introduce a novel adaptive metric distillation approach for superior student network feature enhancement.
• To address the limitations of existing KD methods by incorporating sample relationships.
• To improve performance in both classification and retrieval tasks.

Main Methods:

• Proposed collaborative adaptive metric distillation (CAMD) framework.
• Incorporated a hard mining strategy to optimize key pair relationships within the distillation process.
• Employed adaptive metric distillation for direct optimization of student feature embeddings using teacher embedding relations.
• Utilized a collaborative scheme for efficient knowledge aggregation.

Main Results:

• CAMD significantly enhances student network backbone features.
• Achieved superior classification and retrieval task performance.
• Established a new state-of-the-art across various experimental settings.
• Outperformed existing cutting-edge knowledge distillation methods.

Conclusions:

• The proposed CAMD approach effectively improves student network performance by optimizing feature relationships.
• CAMD demonstrates state-of-the-art results in both classification and retrieval.
• Adaptive metric distillation offers a powerful mechanism for feature embedding optimization in knowledge transfer.