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Improved Knowledge Distillation Framework Achieves 99.04% Accuracy for Land-Use Classification

Artificial Intelligence #knowledge distillation#land-use classification

Improved Knowledge Distillation Framework Achieves 99.04% Accuracy for Land-Use Classification

A research paper on arXiv presents an improved knowledge distillation framework for compressing deep neural networks used in land-use image classification. By integrating hard label supervision with soft losses (KL divergence and cosine similarity), the method achieves 99.04% accuracy on three land-use datasets, outperforming baseline and single-loss distillation approaches while substantially reducing model size.

Jun 16, 2026 1 source