This paper presents a method to explain how input information is discarded through intermediate layers of a neural network during the forward propagation, in order to quantify and diagnose knowledge representations of pre-trained deep neural networks. We define two types of entropy-based metrics, i.e. the strict information discarding and the reconstruction uncertainty, which measure input information of a specific layer from two perspectives. We develop a method to enable efficient computation of such entropy-based metrics. Our method can be broadly applied to various neural networks and enable comprehensive comparisons between different layers of different networks. Preliminary experiments have shown the effectiveness of our metrics in analyzing benchmark networks and explaining existing deeplearning techniques. The code will be released when the paper is accepted.
REF presented a method to quantify the layer-wise information discarding of DNNs and defined two model-agnostic and task-agnostic metrics of the input information.
Quantifying Layerwise Information Discarding of Neural Networks
ArXiv
ArXiv
Computer Science Mathematics