Visualizing CNNs and Deeper Deep Architectures

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Presentation transcript:

Visualizing CNNs and Deeper Deep Architectures

Visualizing Deep Networks See slides here: http://places.csail.mit.edu/slide_iclr2015.pdf

Beyond AlexNet

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Compared to Alex-Net – Deeper, smaller convolutions, more parameters

Compared to Alex-Net – Deeper, smaller convolutions, more parameters

Going Deeper with Convolutions Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich 2015 This is the “Inception” architecture or “GoogLeNet” *The architecture blocks are called “Inception” modules and the collection of them into a particular net is “GoogLeNet”

Only 6. 8 million parameters Only 6.8 million parameters. AlexNet ~60 million, VGG up to 138 million

Surely it would be ridiculous to go any deeper… ResNet

Surely it would be ridiculous to go any deeper… ResNet See the following slides: http://kaiminghe.com/cvpr16resnet/cvpr2016_dee p_residual_learning_kaiminghe.pdf