#012 CNN Convolutional Neural Networks – An Overview
Last time we saw we are Convolutions very useful when we include them in our neural networks. In this post we will give just an overview of the 11 Convolutional Neural Network architectures.
| Convolutional Neural Netwok | Academic Paper | Authors | Python experiments |
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” Gradient-based Learning Applied to Document Recognition “ | Yann LeChun, Leon Bottou, Youshua Bengaio, Patric Haffner | |
| AlexNet | “ImageNet Classification with Deep Convolutional Neural Networks” | Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton | |
| VGG16 and VGG19 | ” Very Deep Convolutional Neural Networks for Large Scale Image Recognition “ | Karen Simonyan, Andrew Zisserman | |
| Residual Networks | ” Deep Residual Learning for Image Recognition “ | Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun | |
| Inception Network | ” Going deeper with convolutions “ | Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent | |
| Squeez Net | “SqueezeNet: AlexNet-Level accuracy with 50x fever parameters and <0.5MB model size” | Forrest N. Iandola , Song Han , Matthew W. Moskewicz , Khalid Ashraf ,William J. Dally , Kurt Keutzer |
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| Spatial Transformer Networks | ” Spatial Transformer Networks “ | Max Jaderberg, Karen Simonyan , Andrew Zisserman, Koray Kavukcuoglu | |
| DenseNet | ” Densely Connected Convolutional Networks “ | Gao Huang, Zhuang Liu, Laurens van der Maaten | |
| Siamese network | ” Learning a Similarity Metric Discriminatively, with Application to Face Verification “ | Sumit Chopra, Raia Hadsell, Yann LeCun | |
| The Triplet Network | ” Deep metric learning using Triplet network “ | Elad Hoffer, Nir Ailon | |
| Face Net | ” FaceNet: A Unified Embedding for Face Recognition and Clustering “ |
Florian Schroff, Dmitry Kalenichenko, James Philbin |
In the next post we will start talking about LeNet-5 .
