Microsoft has once again proved that there are areas that you just can not stand against the Redmond giant. In the sixth annual ImageNet image recognition competition, Microsoft has secured first place in several categories, beating out alike programs from Google, Intel, Qualcomm, and Tencent.
Like many other researchers in this field, Microsoft relied on a method called deep neural networks to train computers to recognize the images. Their system was more effective because it allowed them to use extremely deep neural nets, which are as much as five times deeper than any previously used.
The researchers say even they weren’t sure this new approach was going to be successful – until it was. The team explained in technical terms –
We train neural networks with depth of over 150 layers. We propose a ‘deep residual learning’ framework that eases the optimization and convergence of extremely deep networks. Our ‘deep residual nets’ enjoy accuracy gains when the networks are substantially deeper than those used previously. Such accuracy gains are not witnessed for many common networks when going deeper.
The contests, organized by researchers from top universities and corporations, have in the past few years become a leading barometer of success in this exploding field.
The competition at ImageNet required entries to correctly locate and classify objects in 100,000 photographs from Flickr and search engines into 1,000 object categories. Microsoft’s winning entry had a classification error rate of 3.5 percent and a localization error rate of 9 percent.
In the ImageNet challenge, the Microsoft team won first place in all three categories it entered, that is, classification, localization and detection. We can just imagine how powerful the system is that Microsoft has conceived and garnered this victory.
In the Microsoft Common Objects in Context challenge, also known as MS COCO, the Microsoft team won first place for image detection and segmentation. The MS COCO project was originally funded by Microsoft and started as a collaboration between Microsoft and a few universities, but it is now run by academics outside of Microsoft.
This win means a breakthrough in this segment, with Microsoft vigorously working in the direction to remove as many as anomalies as possible. Earlier systems were highly inaccurate, which made them quiet non reliable.
Then, about five years ago, researchers hit upon the idea of using a technology called neural networks, which are inspired by the biological processes of the brain.
The system also proved very successful for recognizing speech, and it’s been the basis for the real-time translation capability in Skype Translator.