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Bridging the accuracy gap between teacher and student models in knowledge distillation is crucial for effective learning, and using a dynamic teacher with bidirectional mappings effectively achieves this, leading to significant performance improvements in compact student models.
Guo, Y., Zhang, S., Pan, H., Liu, J., Zhang, Y., & Chen, J. (2024). GAP PRESERVING DISTILLATION BY BUILDING BIDIRECTIONAL MAPPINGS WITH A DYNAMIC TEACHER (arXiv:2410.04140v1). arXiv. https://arxiv.org/abs/2410.04140v1
This paper addresses the challenge of effectively transferring knowledge from large, complex teacher models to smaller student models in deep neural networks, particularly when a significant performance gap exists between them. The authors aim to develop a novel knowledge distillation method that maintains an appropriate accuracy gap throughout the training process to enhance knowledge transfer and improve student model performance.