Abstract
To solve the catastrophic forgetting problem caused by incremental learning,a deep incremental image classification method based on double-branch iteration is proposed.The primary network is utilized to store the acquired old class knowledge,while the branch network is exploited to learn the new class knowledge.The parameters of the branch network are optimized by the weight of the primary network in the incremental iteration process.Density peak clustering method is employed to select typical samples from the iterative dataset and construct retention set.The retention set is added into the incremental iteration training to mitigate catastrophic forgetting.The experiments demonstrate the better performance of the proposed method.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: 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.
