MACHINE PERCEPTION AND ROBOTICS GROUP

Dept. of Robotics Science and Technology,
Chubu University

Convolutional Neural Networks (CNNs) are being used in a variety of fields centered about image recognition and are achieving high levels of recognition accuracy. However, existing CNNs cannot consider uncertainty in the prediction result, that is, the difficulty of prediction, which means the extent to which the prediction is reliable is unclear. This problem is considered to be the cause of erroneous decisions in the practical use of CNN. In this research, we propose a Bayesian Attention Branch Network (Bayesian ABN) that introduces uncertainty into an Attention Branch Network (ABN). That is to say, the proposed method considers uncertainty in the CNN prediction result by introducing a Bayesian Neural Network (Bayesian NN) into the ABN. In addition, the method focuses on a structure that outputs prediction results from two branches and adopts the result having a lower value of uncertainty. In evaluation experiments using a standard object recognition dataset, we confirmed that the proposed method improves CNN accuracy and reliability.