Balancing Knowledge Distillation for Imbalance Learning with Bilevel Optimization
Knowledge distillation transfers knowledge from a high capacity teacher to a compact student using a mixture of hard and soft losses. On imbalanced data, a fixed weighting between hard and soft losses becomes brittle the learning process. Recent studies try to reweight these components in long-tailed settings. However, most of these methods do not adapt weights at the sample-wise level and do not take into account the students behavior during training. To address this, we propose BiKD -- a bilevel framework that dynamically balances hard and soft losses for each sample. We employ a weight generation network that produces adaptive per-sample weights, guided by a small balanced validation s...
| Authors | Anh B. H. Nguyen, Ba Tho Phan, Viet Cuong Ta |
|---|---|
| Primary category | cs.LG |
| Published | 18 May 2026 |
| Last updated at source | 30 May 2026 |
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