CNN-Architectures

1. LeNet 5

   LeNet-5, from the paper Gradient-Based Learning Applied to Document Recognition, is a very efficient convolutional neural network for handwritten character recognition.

   Paper: http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf

   Authors: Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner

   Published in: Proceedings of the IEEE (1998)

2. Alexnet

   AlexNet competed in the ImageNet Large Scale Visual Recognition Challenge on September 30, 2012. The network achieved a top-5 error of 15.3%, more than 10.8 percentage points lower than that of the runner up. The original paper's primary result was that the depth of the model was essential for its high performance, which was computationally expensive, but made feasible due to the utilization of graphics processing units (GPUs) during training

   paper : https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf

   Authors: Alex Krizhevsky , Ilya Sutskever , Geoffrey E. Hinton

   Published in : 2012

3. VGG

   The full name of VGG is the Visual Geometry Group, which belongs to the Department of Science and Engineering of Oxford University. It has released a series of convolutional network models beginning with VGG, which can be applied to face recognition and image classification, from VGG16 to VGG19. The original purpose of VGG's research on the depth of convolutional networks is to understand how the depth of convolutional networks affects the accuracy and accuracy of large-scale image classification and recognition. -Deep-16 CNN), in order to deepen the number of network layers and to avoid too many parameters, a small 3x3 convolution kernel is used in all layers.

   paper : https://arxiv.org/pdf/1409.1556.pdf

   Authors: Karen Simonyan , Andrew Zisserman

   Published in : 2013