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Multi-task Self-Supervised Visual Learning

Carl Doersch and Andrew Zisserman in ICCV 2017 [arXiv] [Show BibTex]

Supervision Beyond Manual Annotations for Learning Visual Representations

Carl Doersch.
Carnegie Mellon Thesis Dissertation [pdf] [Show BibTex]

Tutorial on Variational Autoencoders

Carl Doersch.
Arxiv Tech Report, June 2016 [arXiv] [Show BibTex]

An Uncertain Future: Forecasting from Static Images using Variational Autoencoders

Jacob Walker, Carl Doersch, Abhinav Gupta, and Martial Hebert.
in ECCV 2016 [webpage] [arXiv] [Show BibTex]

Data-dependent Initializations of Convolutional Neural Networks

Philipp Krähenbühl, Carl Doersch, Jeff Donahue, and Trevor Darrell.
ICLR, 2016 [arxiv]

Unsupervised Visual Representation Learning by Context Prediction

Carl Doersch, Abhinav Gupta, and Alexei A. Efros.
in ICCV 2015 (oral) [webpage] [arXiv] [Show BibTex]

Context as Supervisory Signal: Discovering Objects with Predictable Context

Carl Doersch, Abhinav Gupta, and Alexei A. Efros.
In ECCV 2014 [Show BibTex]

Mid-Level Visual Element Discovery as Discriminative Mode Seeking

Carl Doersch, Abhinav Gupta, and Alexei A. Efros.
In NIPS 2013 [Show BibTex]

What Makes Paris Look like Paris?

Carl Doersch, Saurabh Singh, Abhinav Gupta, Josef Sivic, and Alexei A. Efros.
In SIGGRAPH 2012 (oral) Republished on the cover of the CACM magazine Dec. 2015 [Show BibTex]

Bounding the Probability of Error for High Precision Optical Character Recognition

Gary B. Huang, Andrew Kae, Carl Doersch, and Erik Learned-Miller.
In JMLR 2012 [pdf] [Show BibTex]

Improving state-of-the-art OCR through high-precision document-specific modeling.

Andrew Kae, Gary B. Huang, Carl Doersch, and Erik Learned-Miller.
In CVPR 2010 [pdf] [Show BibTex]