ECCV Paper IDeaL: Data-free multi-teacher distillation via improved dead leaves
RexDouglass · x · 2026-09-02
Accepted at ECCV 2026, the paper 'IDeaL' proposes a data-free multi-teacher knowledge distillation method. By using optimized structured noise, it successfully distills knowledge from multiple vision teachers into a single student model without real images, significantly narrowing the performance gap with real-image distillation and surpassing it on limited data budgets.
Related event: ECCV Paper IDeaL Distills Four Vision Teachers with Zero Real Images(4 posts)→
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