Knowledge-Aware Neuron Interpretation for Scene Classification
Yong Guan Freddy Lecue Jiaoyan Chen Ru Li Jeff Z. Pan
Although neural models have achieved remarkable performance, they still
encounter doubts due to the intransparency. To this end, model prediction
explanation is attracting more and more attentions. However, current methods
rarely incorporate external knowledge and still suffer from three limitations:
(1) Neglecting concept completeness. Merely selecting concepts may not
sufficient for prediction. (2) Lacking concept fusion. Failure to merge
semantically-equivalent concepts. (3) Difficult in manipulating model behavior.
Lack of verification for explanation on original model. To address these
issues, we propose a novel knowledge-aware neuron interpretation framework to
explain model predictions for image scene classification. Specifically, for
concept completeness, we present core concepts of a scene based on knowledge
graph, ConceptNet, to gauge the completeness of concepts. Our method,
incorporating complete concepts, effectively provides better prediction
explanations compared to baselines. Furthermore, for concept fusion, we
introduce a knowledge graph-based method known as Concept Filtering, which
produces over 23% point gain on neuron behaviors for neuron interpretation. At
last, we propose Model Manipulation, which aims to study whether the core
concepts based on ConceptNet could be employed to manipulate model behavior.
The results show that core concepts can effectively improve the performance of
original model by over 26%.