Microsoft Achieves Substantial Beyond-Human-Level Deep Learning Advance

Tuesday, February 10, 2015

Microsoft Achieves Substantial Beyond Human Level Deep Learning Advance

 Deep Learning
A new computer vision system based on deep convolutional neural networks has for the first time eclipsed the abilities of humans to classify objects. The Microsoft researchers claim their system achieved a 4.94 percent error rate on the ImageNet database. Humans tested on the same system averaged  5.1 percent.

Researchers at Microsoft claim their latest deep learning computer vision system can outperform humans in image recognition.

In their paper, "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification," the Asian-based Microsoft Research developers say their system achieved  a 4.94 percent error rate for the correct classification of images in the 2012 version of the widely recognized ImageNet data set, compared with a 5.1 percent error rate among humans.

The challenge involved identifying objects in the images and then correctly selecting the most accurate categories for the images, out of 1,000 options. Categories included “hatchet,” “geyser,” and “microwave.”
“To the best of our knowledge, our result surpasses for the first time the reported human-level performance on this visual recognition challenge,” Microsoft researchers Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun wrote in the paper.

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Deep learning involves training artificial neural networks on lots of information derived from images, audio, and other inputs, and then presenting the systems with new information and receiving inferences about it in response

"To the best of our knowledge, our result surpasses for the first time the reported human-level performance on this visual recognition challenge."

The research builds on the company's other impressive deep learning demonstrations of Project Adam, which was first demonstrated last year.

Along with surpassing human capability, the new system from Microsoft researchers improves on Google’s award-winning GoogLeNet system by 26 percent, as it performed with 6.66 percent error, the Microsoft researchers claim.

In a bit of modesty, the researchers noted that they don’t feel computer vision trumps human vision.

“While our algorithm produces a superior result on this particular dataset, this does not indicate that machine vision outperforms human vision on object recognition in general,” they wrote. “On recognizing elementary object categories (i.e., common objects or concepts in daily lives) such as the Pascal VOC task, machines still have obvious errors in cases that are trivial for humans. Nevertheless, we believe that our results show the tremendous potential of machine algorithms to match human-level performance on visual recognition.”

There is no word yet from Microsoft if this development will be used in Cortana, or in the upcoming release of Windows 10.

SOURCE  Microsoft Research

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