Collaborative Knowledge Distillation via Multiknowledge Transfer.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Knowledge distillation (KD) is a key model compression technique in deep learning.
• Existing KD methods often transfer only one type of knowledge, limiting learning capacity.
• Offline distillation faces limitations due to fixed teacher-student architectures.

Purpose of the Study:

• To propose a novel collaborative knowledge distillation (CKD-MKT) framework.
• To enable multi-knowledge transfer and collaborative learning.
• To overcome limitations of existing KD methods.

Main Methods:

• Developed a unified framework for self-learning and collaborative learning.
• Implemented a multiple knowledge transfer framework with self and online distillation strategies.
• Enabled students to learn from instance features and relations, and from each other.

Main Results:

• CKD-MKT effectively fuses diverse knowledge types.
• The framework facilitates mutual guidance through collaborative and self-learning.
• Experiments on six image datasets showed significant performance improvements over state-of-the-art KD methods.

Conclusions:

• CKD-MKT offers a more effective approach to knowledge distillation.
• The multi-knowledge transfer and collaborative learning enhance model compression.
• This method advances the field of efficient deep learning models.