C2R-KD: Complex to Real Knowledge Distillation (Student Abstract)
Abstract
In this work, C2R-KD is proposed, applying a Complex-to-Real projection to map complex domain features into the real domain. C2R-KD mitigates complex-real domain mismatch to strengthen the representational capacity of the student model and further improves the knowledge distillation model performance through the hybrid distillation of features and logits simultaneously. Experimental result demonstrates higher accuracy than the conventional KD across all test environments.
Fields of Study & Subjects
Advanced Neural Network Applications
Domain Adaptation and Few-Shot Learning
Machine Learning and Data Classification
Publication details
- Journal / source
- Underline Science Inc.
- Publication date
- 7 January 2026
- Language
- EN