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The presentation is coverong the convolution neural network (CNN) design.
First,
the main building blocks of CNNs will be introduced. Then we systematically
investigate the impact of a range of recent advances in CNN architectures and
learning methods on the object categorization (ILSVRC) problem. In the
evaluation, the influence of the following choices of the architecture are
tested: nonlinearity (ReLU, ELU, maxout, compatibility with batch
normalization), pooling variants (stochastic, max, average, mixed), network
width, classifier design (convolution, fullyconnected, SPP), image
preprocessing, and of learning parameters: learning rate, batch size,
cleanliness of the data, etc.
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