Neural network research at Google X
Unsupervised learning on a 1,600-core cluster

Google have built a 9-layer neural network that can learn to detect faces using only unlabeled images. The model has 1 billion connections and the dataset has 10 million 200x200 pixel images randomly downloaded from the internet. It was trained on a cluster of 1,000 machines (16,000 cores) for three days.

The results show it's possible to train a face detector without having to label images as containing a face or not. The detector is robust to translation, scaling, and out-of-plane rotation. It's also sensitive to other high-level concepts such as cat faces and human bodies.

NY Times: goo.gl/zPKcl
Paper: arxiv.org/abs/1112.6209
Background: artificialbrains.com/google-x-lab
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