Generate More Training Data When You Don't Have Enough

Computers outperform humans in image and object recognition. Big corporations like Google and Microsoft have beat the human benchmark on image recognition [1, 2]. On average, human makes an error on image recognition tasks about 5% of the time. As of 2015, Microsoft's image recognition software reached an error rate of 4.94%, and at around the same time, Google announced that its software achieved a reduced error rate of 4.8% [3]. This was possible by training deep convolutional neural networks on millions of training examples from ImageNet dataset which contains hundreds of object categories [1].