The performance of corrected learning network for object recognition
Xingcheng Luo, Jianhua Deng, Ruihan Shen, Qunfang Zhou, Junjie Zhang, Kaiyuan Zhang · 2017
Today, artificial intelligence (AI) has become more and more popular in daily life, such as face recognition, speech recognition, automated driving. In this paper, corrected neural model is proposed, where the deep learning framework Caffe is used to verify the proposed model. In this case, a face dataset and a car dataset collected from multiple perspectives are employed. Results show that the proposed corrected network model has great contributes to improve the accuracy of object recognition.