Abstract
The major problem on the road to artificial intelligence is the development of lifelong learning systems. They have the ability to learn new concepts incrementally overtime. They are also able to allocate required resources dynamically without human intervention and are able to store data securely. In this work we have extended the incremental classifier and representation learning method known as iCaRL to meet this criterion. The proposed solution is able to learn strong classifiers and a data representation simultaneously. It is able to allocate an optimal scaling plan to meet its resource requirements without human intervention. It securely stores propriety image data by using state of the art interplanetary file system and block chain technology. Finally, it is able to focus on object of interests in an image using attention network. We have shown by experiments on CIFAR-100 and Image net 2012 that it performs better in terms of accuracy than the existing iCaRL system while satisfying criteria of lifelong learning.
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We would like to extend our acknowledgements to the UM Grand Challenge Project ICT Project No GC003A-14HTM for funding this project.
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Tahir, G.A., Abrar, S., Kiong, L.C. (2020). A Secure Scalable Life-Long Learning Based on Multiagent Framework Using Cloud Computing. In: Arai, K., Bhatia, R. (eds) Advances in Information and Communication. FICC 2019. Lecture Notes in Networks and Systems, vol 70. Springer, Cham. https://doi.org/10.1007/978-3-030-12385-7_31
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DOI: https://doi.org/10.1007/978-3-030-12385-7_31
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