Lecture 9: CNN Architectures
[Krizhevsky et al. 2012] Architecture: CONV1 MAX POOL1 NORM1 CONV2 MAX POOL2 NORM2 CONV3 CONV4 CONV5 Max POOL3 FC6 FC7 FC8. Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 9 - 10 May 2, 2017 Case Study: AlexNet [Krizhevsky et al. 2012] Input: 227x227x3 images First layer (CONV1): 96 11x11 filters applied at stride 4 =>
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