RAPID: Reliability-Aware Pair Importance Distillation
Inter example relational distillation transfers a teacher's representation geometry by matching relations among examples within a mini batch. Computing all pairs has quadratic complexity in the batch size, whereas uniform subsampling may use a limited relation budget inefficiently. We introduce Reliability Aware Pair Importance Distillation, or RAPID, which separates a reliability gated relational target from a full support adaptive pair proposal. Reliability determines which teacher relations are emphasized, while calibrated teacher entropy and detached student-teacher residuals determine which relations are evaluated. Exact inverse proposal correction makes the loss and gradient estimat...
| Authors | Ali Mahdavi, Azadeh Zamanifar, Amirfarhad Farhadi et al. |
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| Primary category | cs.AI |
| Published | 23 August 2026 |
| Last updated at source | 23 August 2026 |
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