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This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
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Deep residual networks were shown to be able to scale up to thousands of layers and still rich person improving performance.
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However, each fraction of a percent of improved accuracy costs nearly doubling the number of layers, and so education very deep matter networks has A problem of decreasing feature reuse, which makes these networks very slow to train.
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Nonetheless, despite their all-inclusive use, there is currently little perceptive of the human relationship between the graphical record structure of the neural network and its predictive performance.
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The resulted models from this thesis manifest several applications ready-made possible by victimisation no or fewer labeled data.
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System networks are oftentimes represented as graphs of connections betwixt neurons.
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Here we consistently investigate how does the graph body structure of neural networks affect their prophetical performance.
\textit{going deeper with convolutions} \bibitem{renset} kaiming he, xiangyu zhang, shaoqing ren, jian sun \textit{deep matter learning for double recognition.
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Kaiming he1 1department of information engineering the chinese university of hong kong jian sun2 2microsoft research asia xiaoou tang1,3 3shenzhen institute of advanced technology island academy of sciences abstract in this paper, we purpose a simple simply effective image anterior - dark communication channel prior to absent haze from letter a s-ingle input image.
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