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Scaling Multi-Teacher Distillation for Digital Pathology
Not provided in the abstract
distillationdigital pathologymachine learningmodel efficiency
2610.12574
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8h ago80%
Abstract
This paper presents a method for improving multi-teacher distillation in digital pathology by learning teacher-specific representations, enabling more efficient training of student encoders.
Reality Card
Core Claim
The proposed method allows for training compact student encoders that are up to 10x more efficient than larger teacher foundation models while matching or outperforming their performance.
Method / Result
Achieved up to 10x efficiency in training compact student encoders compared to larger teacher models.
Limitations
The paper does not specify potential limitations or reproducibility concerns.
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