Semantic coherence analysis of deep neural networks

Abstract

Methods and apparatus are disclosed for interpreting a deep neural network (DNN) using a Semantic Coherence Analysis (SCA)-based interpretation technique. In embodiments, a multi-layered DNN that was trained for one task is analyzed using the SCA technique to select one layer in the DNN that produces salient features for another task. In embodiments, the DNN layers are tested with test samples labeled with a set of concept labels. The output features of a DNN layer are gathered and analyzed according to the concepts. In embodiments, the output is scored with a semantic coherence score, which indicates how well the layer separates the concepts, and one layer is selected from the DNN based on its semantic coherence score. In some embodiments, a support vector machine (SVM) or additional neural network may be added to the selected layer and trained to generate classification results based on the outputs of the selected layer.