A kernel-matrix SVD followed by average pooling gives a controllable, high-compression representation of convolutional activations that preserves out-of-distribution and adversarial-attack detection performance.
Explaining Deep Neural Networks using Unsupervised Clustering
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abstract
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 applied flexibly 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 the strength of our method in finding similar training samples, and shedding light on the concepts the DNNs base their decisions on. Via user studies, we show that our model can improve the user trust in model's prediction.
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A Convolutional Layer Activation Dimensionality Reduction for Out-of-Distribution and Adversarial Attack Detection Methods
A kernel-matrix SVD followed by average pooling gives a controllable, high-compression representation of convolutional activations that preserves out-of-distribution and adversarial-attack detection performance.