【推荐】图像分类必读开创性论文汇总

Deep Learning models for Image Classification have achieved an exponential decline in error rate through last few years. Since then, Deep Learning has become prime focus area for AI research. However, Deep Learning has been around for a few decades now. Yann Lecun, presented a paper pioneering the Convolutional Neural Networks (CNN) in 1998. But it wasn’t until the start of the current decade that Deep Learning really took off. The recent disruption can be attributed to increased processing power (aka GPUs), the availability of abundant data (aka Imagenet dataset) and new algorithms and techniques. It all started in 2012 with the AlexNet, a large, deep Convolutional Neural Network which won the annual ImageNet Large Scale Visual Recognition Challenge (ILSVRC). ILSVRC is a competition where research teams evaluate their algorithms on the given data set and compete to achieve higher accuracy on several visual recognition tasks.
Since then, variants of CNNs have dominated the ILSVRC and have surpassed the level of human accuracy, which is considered to lie in the 5-10% error range.