- Sercan Arik
- Yu-Han Liu
We propose a novel method to explain trained deep neural networks (DNNs), by distilling them into surrogate models using unsupervised clustering. Our method can be flexibly applied to any subset of layers of a DNN architecture and can incorporate low-level and high-level information. On image datasets given pre-trained DNNs, we demonstrate strength of our method in finding similar training samples, and shedding light on the concepts the DNN bases its decision on. Via user studies, we show that our model can improve user trust in model’s prediction.