The VGG-Net model

VGG-Net was introduced by Karen Simonyan and Andrew Zisserman from Visual Geometry Group (VGG) of the University of Oxford. They used small convolutional filters of size 3 x 3 to train a network of depth 16 and 19. Their team secured first and second place in the localization and classification tasks, respectively, of ImageNet Challenge 2014.

The idea to design a deeper neural network by adding more non-linearity to the model led to incorporate smaller filters to make sure the network didn't have too many parameters. While training, it was difficult to converge the model, so first a pre-trained simpler neural net model was used to initialize the weights of the deeper architecture. However, now we can directly use the xavier initialization method instead of training a neural network to initialize the weights. Due the depth of the model, it's very slow to train.