Very Deep Convolutional Networks for Large-Scale Image Recognition
Interesting idea of stacking multiple 3x3 conv+ReLU before pooling for a bigger filter size with just a few parameters. There is also a nice table for "ConvNet Configuration".
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Going Deeper with Convolutions cv-foundation.org
GoogLeNet: Appearance of "Inception" layers/modules, the idea is of parallelizing conv layers into many mini-conv of different size with "same" padding, concatenated on depth.
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ImageNet Classification with Deep Convolutional Neural Networks cs.toronto.edu
AlexNet, 2012 ILSVRC, breakthrough of the ReLU activation function.
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Scaling up Deep Learning videolectures.net
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Deep Residual Learning for Image Recognition arxiv.org
Very deep residual layers with batch normalization layers - a.k.a. "how to overfit any vision dataset with too many layers and make any vision model work properly at recognition given enough data".
Education › Papers > Convolutional Neural Networks: “Interesting idea of stacking multiple 3x3 conv+ReLU before pooling for a bigger filter size with just a few parameters. There is also a nice table for "ConvNet Configuration".”