Progressive Learning Scheduling for Knowledge Distillation

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Authors: Ayoub Oulad Ali
Cited by: 0

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