Important CNN architectures
Here is a list of several important CNN architectures:


LeNet-5 the first successful application of CNNs to digit recognition,   developed by Yann LeCun in 1990. It consists of a sequence of Convolutional, ReLU, Max Pooling layers followed by a Fully Connected layer.
Reference: Y. LeCun et. al., "Gradient-Based Learning Applied to Document Recognition", Proceedings of the IEEE, 1998
AlexNet popularized CNNs in computer vision, did really well on the ImageNet ILSVRC challenge in 2012 showing significant gains in performance.
The network has similar architecture to LeNet but is deeper and bigger and features convolutional layers stacked on top of each other. Reference: A. Krizhevsky, et. al. "ImageNet Classification with Deep Convolutional
Neural Networks", NIPS, 2012
VGG16 demonstrated the importance of depth as a critical component to good performance, it was a runner-up in ILSVRC 2014. The architecture consists of a stacked convolutional and max pooling layers with increasing depth and 
it uses a large number of parameters due to the final fully connected layers. Reference: K. Simonyan, "Very Deep Convolutional Networks for Large-Scale Image Recognition", ICLR, 2015
Inception original version called GoogLeNet from Google won the ILSVRC 2014 challenge the architecture consists of inception modules that dramatically reduce the number of parameters, it uses multi-scale 3x3, 5x5 convolutional filters including 1x1 convolutions for dimensionality reduction. Reference: C. Szegedy et al., "Going Deeper with Convolutions", CVPR, 2015
ResNet50 residual network was the winner of ILSVRC 2015, it introduces skip connections for easier training that enable very deep architectures
and makes use of batch normalization. Reference: K. He et al., "Deep Residual Learning for Image Recognition", CoRR, 2015