Advancements in Knowledge Distillation and Multi-Teacher Learning: Introducing AM-RADIO Framework

Knowledge Distillation has become a prominent technique for transferring knowledge from a “teacher” to a smaller “student” model, surpassing the teacher’s performance. This approach has extended to ensemble distillation and Foundation Models (FMs) like CLIP and DINOv2, known for their zero-shot capabilities and instance segmentation prowess. NVIDIA introduces AM-RADIO, utilizing multiple foundational models simultaneously, outperforming original teachers like CLIP and DINOv2. E-RADIO, a novel hybrid architecture, emerges to address the challenge of distilling FMs with CNN-like architectures. Through multi-teacher distillation, AM-RADIO trains a vision foundation model from scratch, demonstrating superior performance in various tasks, including vision question answering and instance segmentation.

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