Progressive Learning Scheduling for Knowledge Distillation
Abstract
Knowledge Distillation (KD) transfers the knowledge of a large pretrained teacher model into a smaller student model, but the training curriculum—i.e., the schedule and weighting of the distillation signal—remains an open design question. We introduce PLS-KD (Progressive Learning Scheduling for Knowledge Distillation), a family of curriculum-based distillation strategies that modulate how soft targets from a 128-hour teacher are presented to a 32-hour student over 200 epochs.
Fields of Study & Subjects
Advanced Neural Network Applications
Domain Adaptation and Few-Shot Learning
Machine Learning and Data Classification
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 4 March 2026
- Language
- EN