Progressive Knowledge Distillation Boosts Model Compression Efficiency

Researchers have developed a new knowledge distillation technique called Progressive^2 to significantly improve model compression, particularly when there's a substantial difference in capabilities between a large server-side model (teacher) and a much smaller client-side model (student). Traditional distillation methods often struggle in such scenarios, leading to compromised performance. The Progressive^2 method involves a dual progressive strategy. On the teacher side, knowledge is transferred by progressively selecting additional layers for distillation, following a raw-to-rich semantic progression, and incorporating a multi-feature fusion adapter for stability. On the student side, instead of directly training a tiny model, its size is gradually reduced, allowing for an iterative co-evolution with the teacher. This flexible framework allows the teacher's progressive strategy to be deployed independently for optimal accuracy and training efficiency. When both teacher and student strategies are integrated, further improvements in overall performance are observed, offering a robust solution for substantial model compression.