Abstract
In reality, an information processing system needs to receive data from various sensors. This is called a hybrid information system. Image recognition is widely applied to hybrid information systems. Recently, Network in Network (NIN) as a special deep learning model has obtained impressive results on image recognition. However, there is a lot of room for performance improvement, since gradient-based optimization methods may fall into local minima. To get a high recognition rate, it is essential to explore the solution space further. In this paper, we propose a hybrid optimization method for NIN in order to improve the image recognition rate. A fine-tuning strategy was employed to train NIN by using particle swarm optimization followed by a pre-training stage based on the gradient method. Several strategies were introduced in order to avoid the deterioration of the solution. The results showed that the present method can effectively increase the image recognition rate and be further used in a hybrid information system.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.