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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. Contrary to what appears tobe a widely-held intuition, our experimentalresults reveal that it is possible to train a facedetector without having to label images ascontaining a face or not.
Building high-level features using large-scale unsupervised learning volutional DBNs (Lee et al.,2009), trained on aligned images of faces, can learn a face detector.
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