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Abstract 1. Introduction
Building High-level FeaturesUsing Large Scale Unsupervised LearningQuoc V. Aurelio S. Y. consider the problem of building high-level, class-specific feature detectors fromonly unlabeled data. For example, is it pos-sible to learn a face detector using only unla-beled images? To answer this, we train a 9-layered locally connected sparse autoencoderwith pooling and local contrast normalizationon a large dataset of images (the model has1 billion connections, the dataset has 10 mil-lion 200x200 pixel images downloaded fromthe Internet). We train this network usingmodel parallelism and asynchronous SGD ona cluster with 1,000 machines (16,000 cores)for three days.
Building High-level Features Using Large Scale Unsupervised Learning Quoc V. Le quocle@cs.stanford.edu Marc’Aurelio Ranzato ranzato@google.com
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