Convolutional Neural Networks - AI Summary

Research by Hubel and Wiesel [2,3] analyzed the striate cortex of cats and monkeys, revealing two key findings that would come to heavily influence Fukushima's work [1]. The next significant implementation of a convolution neural network was LeNet-5 proposed in 1999 by Le Cun et al. in their work "Object Recognition with Gradient Based Learning'' [4]. Their proposed network, LeNet-5 performed well on the MNIST data set and was shown to do better than state of the art (at the time) SVMs and K-nearest neighbor based approaches. Their final implementation outperformed other state of the art image classification algorithms with error rates which were 10% lower than its competitors on the ImageNet dataset. This application of a discrete convolution precisely represents local receptive fields observed by Hubel and Wiesel [2,3] and implemented in early CNNs by Fukushima and Le Cun [1,4]. Research by Hubel and Wiesel [2,3] analyzed the striate cortex of cats and monkeys, revealing two key findings that would come to heavily influence Fukushima's work [1]. The next significant implementation of a convolution neural network was LeNet-5 proposed in 1999 by Le Cun et al. in their work "Object Recognition with Gradient Based Learning'' [4].