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Modeling Local and Global Deformations in Deep Learning: Epitomic Convolution, Multiple Instance Learning, and Sliding Window Detection George Papandreou Google Iasonas Kokkinos and Pierre-Andr´e Savalle
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Document Date: 2015-05-25 21:19:04


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City

Oxford / Chicago / /

Company

Epitomic Convolutional Networks / Deep Convolutional Neural Networks / GPU / NVIDIA Corporation / Abstract Deep Convolutional Neural Networks / CNN / /

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Event

Reorganization / /

Facility

Toyota Technological Institute / pyramid I / University of Toronto DCNN / /

IndustryTerm

selective search / hole algorithm / inner products / deformation modeling tools / real-time applications / trous algorithm / joint search / region proposal algorithm / selective search windows / aspect ratio search / signal processing / convolutional neural network / exhaustive search / convolutional networks / a` trous algorithm / convolutional network / scale/position search net / position search / convolutional neural networks / deep networks / explicit search / computing / layer classification network / deep network / recognition algorithms / baseline network / deep neural networks / detection using convolutional networks / using more standard deformation modeling tools / /

Organization

U.S. Securities and Exchange Commission / AP (mAP) / University of Toronto DCNN / US Federal Reserve / Toyota Technological Institute / European Union / NYU / /

Person

Fergus Overfeat / George Papandreou Google Iasonas Kokkinos / Max-Pool Epitomic Epitomic Epitomic Epitomic / D. Erhan / V / /

Position

MP / model deformations / chair / /

Product

Pentax K-x Digital Camera / Caffe / Titan / /

ProgrammingLanguage

MATLAB / Pascal / /

Technology

neuroscience / region proposal algorithm / Sliding windows / machine learning / hole algorithm / trous algorithm / neural network / aero / a` trous algorithm / recognition algorithms / Instance Learning algorithm / /

URL

http /

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