Abstract
Many supervised deep learning architectures have evolved over the last few years, achieving top scores on many tasks. Deep learning architectures can achieve high accuracy; sometimes, it can exceed human-level performance. Supervised training of convolutional neural networks, which contain many layers, is done by using a large set of labeled data. Some of the supervised CNN architectures proposed by researchers include LeNet-5, AlexNet, ZFNet, VGGNet, GoogleNet, ResNet, DenseNet, and CapsNet. These architectures are briefly discussed in this chapter.
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Wani, M.A., Bhat, F.A., Afzal, S., Khan, A.I. (2020). Supervised Deep Learning Architectures. In: Advances in Deep Learning. Studies in Big Data, vol 57. Springer, Singapore. https://doi.org/10.1007/978-981-13-6794-6_4
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DOI: https://doi.org/10.1007/978-981-13-6794-6_4
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