5 Neural network architectures you must know for Computer Vision

The idea of convolutions was first introduced by Kunihiko Fukushima in this paper. The neocognitron introduced 2 types of layers, convolutional layers and downsampling layers. Then next key advancement was by Yann LeCun et al. when they used back-propagation to learn the coefficients of the convolutional kernel from images. This made learning automatic and not laboriously handcrafted. According to Wikipedia, this approach became a foundation for modern computer vision.