Adaptive wavelet distillation from neural networks through interpretations

Understand

Recent deep-learning models have achieved impressive prediction performance, but often sacrifice interpretability and computational efficiency.

• Interpretability is crucial in many disciplines, such as science and medicine, where models must be carefully vetted or where interpretation is the goal itself.
• Moreover, interpretable models are concise and often yield computational efficiency.
• Here, we propose adaptive wavelet distillation (AWD), a method which aims to distill information from a trained neural network into a wavelet transform.