Hide-and-Seek: A Template for Explainable AI

Abstract

Lack of transparency has been the Achilles heal of Neural Networks and their wider adoption in industry. Despite significant interest this shortcoming has not been adequately addressed. This study proposes a novel framework called Hide-and-Seek (HnS) for training Interpretable Neural Networks and establishes a theoretical foundation for exploring and comparing similar ideas. Extensive experimentation indicates that a high degree of interpretability can be imputed into Neural Networks, without sacrificing their predictive power.

24 pages, 14 figures. Submitted on a special issue for Explainable AI, on Elsevier's "Artificial Intelligence"

References in corpus (4)

• Distilling the Knowledge in a Neural Network
• Recurrent Models of Visual Attention
• Multiple Object Recognition with Visual Attention
• Hierarchical Multiscale Recurrent Neural Networks