Interpretation Support System for Classification Patterns from Deep Learning Networks using HMM

Abstract

In deep learning, there is a problem that concrete classification patterns for deriving reasons for classification are often incomprehensible. In this paper, we propose a classification patterns extraction system from deep learning networks and verified the effectiveness of the system. The proposed system extracts classification patterns from the trained learning networks of LSTM using HMM. Then the system displays the extracted classification patterns so that users of the system can interpret the learning networks. In the verification experiments, the interpretations of the extracted classification patterns were compared with the interpretations of the classification patterns based on the TFIDF ranking. The results showed that the proposed system can extract classification patterns effective for interpretations of the learning networks.