Abstract
Artificial Intelligence aims to mimic natural intelligent learning by using lifelong machine learning, which allows an AI to train and learn over its lifetime. Various algorithms have been suggested and developed to allow lifelong learning, these algorithms require deeper analysis, to evaluate and highlight performance benefits. In this research, we will study three state-of-the-art algorithms for lifelong learning: Rehearsal, elastic weight consolidation and synaptic intelligence. We do an analysis and evaluation of their performance in a multiple-task experiment, using different amounts of data, and measuring several performance metrics. We found that these algorithms are similar in performance, but some algorithms perform better than others with less data, or show good performance in task one, but not subsequent tasks. These algorithms could be built upon and improved in future research. The evaluation demonstrated in this research is in the image classification context.
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.
