Representational Difference Distillation
Knowledge distillation transfers representational knowledge from a large teacher network to a compact student. Existing relational and contrastive distillation methods treat all samples uniformly, spending equal gradient on well-matched and poorly-matched regions of the representation space alike. We propose Representational Difference Distillation (RDD), which diagnoses where teacher and student representations structurally disagree and concentrates the distillation signal on those regions. RDD uses two asymmetric affinity matrices that capture opposing directions of misalignment: one identifies samples the student incorrectly considers similar, the other identifies teacher relationships...
| Authors | Abdulkarim Mugisha, Divin Irakiza |
|---|---|
| Primary category | cs.LG |
| Published | 11 October 2026 |
| Last updated at source | 11 October 2026 |
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