A 1.96M-parameter LiteDenoiseNet student model achieves 37.58 dB PSNR on full-resolution real image denoising benchmarks while running in 34-46 ms on mobile NPUs by leveraging NPU-compatible primitives and high-alpha knowledge distillation.
Fitnets: Hints for thin deep nets
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Teacher-guided routing supplies pseudo-supervision from a dense model's intermediate features to stabilize expert selection in sparse vision MoE models.
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Real Image Denoising with Knowledge Distillation for High-Performance Mobile NPUs
A 1.96M-parameter LiteDenoiseNet student model achieves 37.58 dB PSNR on full-resolution real image denoising benchmarks while running in 34-46 ms on mobile NPUs by leveraging NPU-compatible primitives and high-alpha knowledge distillation.
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Teacher-Guided Routing for Sparse Vision Mixture-of-Experts
Teacher-guided routing supplies pseudo-supervision from a dense model's intermediate features to stabilize expert selection in sparse vision MoE models.