Going Deeper with Convolutions
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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Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning arxiv.org
For improving GoogLeNet with residual connections.
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Inceptionism: Going Deeper into Neural Networks@Google Research googleresearch.blogspot.kr
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Very Deep Convolutional Networks for Large-Scale Image Recognition arxiv.org
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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Improving Inception and Image Classification in TensorFlow research.googleblog.com
Very interesting CNN architecture (e.g.: the inception-style convolutional layers is promising and efficient in terms of reducing the number of parameters).
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Scaling up Deep Learning videolectures.net
Education › Papers > Convolutional Neural Networks: “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.”