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Learning Convolutional Feature Hierarchies for Visual Recognition
koray kavukcuoglu · Pierre Sermanet · Y-Lan Boureau · Karol Gregor · Michael Mathieu · Yann LeCun

Tue Dec 07 11:20 AM -- 11:25 AM (PST) @ Regency Ballroom

We propose an unsupervised method for learning multi-stage
hierarchies of sparse convolutional features. While sparse coding
has become an increasingly popular method for learning visual
features, it is most often trained at the patch level. Applying the
resulting filters convolutionally results in highly redundant codes
because overlapping patches are encoded in isolation. By training
convolutionally over large image windows, our method reduces the
redudancy between feature vectors at neighboring locations and
improves the efficiency of the overall representation. In addition
to a linear decoder that reconstructs the image from sparse
features, our method trains an efficient feed-forward encoder that
predicts quasi-sparse features from the input. While patch-based
training rarely produces anything but oriented edge detectors, we
show that convolutional training produces highly diverse filters,
including center-surround filters, corner detectors, cross
detectors, and oriented grating detectors. We show that using these
filters in multi-stage convolutional network architecture improves
performance on a number of visual recognition and detection tasks.

Author Information

koray kavukcuoglu (DeepMind)
Pierre Sermanet (New York University)
Y-Lan Boureau (Facebook)
Karol Gregor (Google DeepMind)
Michael Mathieu
Yann LeCun (Facebook AI Research and New York University)

Yann LeCun is Director of AI Research at Facebook, and Silver Professor of Data Science, Computer Science, Neural Science, and Electrical Engineering at New York University. He received the Electrical Engineer Diploma from ESIEE, Paris in 1983, and a PhD in Computer Science from Université Pierre et Marie Curie (Paris) in 1987. After a postdoc at the University of Toronto, he joined AT&T Bell Laboratories in Holmdel, NJ in 1988. He became head of the Image Processing Research Department at AT&T Labs-Research in 1996, and joined NYU as a professor in 2003, after a brief period as a Fellow of the NEC Research Institute in Princeton. From 2012 to 2014 he directed NYU's initiative in data science and became the founding director of the NYU Center for Data Science. He was named Director of AI Research at Facebook in late 2013 and retains a part-time position on the NYU faculty. His current interests include AI, machine learning, computer perception, mobile robotics, and computational neuroscience. He has published over 180 technical papers and book chapters on these topics as well as on neural networks, handwriting recognition, image processing and compression, and on dedicated circuits for computer perception.

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