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Evolving Image Classification Architectures With Enhanced Particle Swarm Optimisation
2018
IEEE Access
Convolutional Neural Networks (CNNs) have become the de facto technique for image feature extraction in recent years, however their design and construction remains a complicated task. As more developments are made in progressing the internal components of CNNs, the task of assembling them effectively from core components becomes even more arduous. To overcome these barriers, we propose Swarm Optimised Block Architecture (SOBA), combined with an enhanced adaptive Particle Swarm Optimisation
doi:10.1109/access.2018.2880416
fatcat:wihnugqkqjcozhxzz6qvt6lrbe