The race for ever-increasing discriminative power in image classification has been heating up over the last period.
2 days ago the chinese Baidu search company announced that they beat the previous record in image recognition set by Microsoft Research, by a marginal 0.36% less error rate. Microsoft was the first to surpass human recognition performance almost 3 months ago in February 2015, with Google currently holding the 2nd best recognition performance.
All this is made possible through the use of deep convolutional networks and deep learning schemes, namely, the construction of neuromorphic recognition schemes where raw information passes through multiple intermediate layers before giving the desired class recognition output. This is made possible by using immense computational power (super-computers) which is directed into training a system onto huge amounts of ground-truth data.
These news come as a follow-up to the previous post on human emotion emulation and recognition where scientists reported that the corresponding system could reach and marginally exceed the human recognition performance of emotions!
For those interested, you may have a look at the news talking about the technological breakthrough here:
and at the arxiv repository for corresponding scientific documentation on the respective systems: