C2R-KD: Complex to Real Knowledge Distillation (Student Abstract)

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Authors: Association for Artificial Intelligence 2026, 윤병혁 ., Ohyun Jo
Cited by: 0

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