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Pierre Sermanet
Researcher at Google
Publications - 66
Citations - 53384
Pierre Sermanet is an academic researcher from Google. The author has contributed to research in topics: Feature learning & Computer science. The author has an hindex of 29, co-authored 56 publications receiving 40360 citations. Previous affiliations of Pierre Sermanet include New York University & Courant Institute of Mathematical Sciences.
Papers
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Proceedings ArticleDOI
Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
Proceedings Article
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
TL;DR: In this article, a multiscale and sliding window approach is proposed to predict object boundaries, which is then accumulated rather than suppressed in order to increase detection confidence, and OverFeat is the winner of the ImageNet Large Scale Visual Recognition Challenge 2013.
Posted Content
Going Deeper with Convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
TL;DR: A deep convolutional neural network architecture codenamed Inception is proposed that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
Posted Content
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
TL;DR: This integrated framework for using Convolutional Networks for classification, localization and detection is the winner of the localization task of the ImageNet Large Scale Visual Recognition Challenge 2013 and obtained very competitive results for the detection and classifications tasks.
Posted Content
Pedestrian Detection with Unsupervised Multi-Stage Feature Learning
TL;DR: This work reports state-of-the-art and competitive results on all major pedestrian datasets with a convolutional network model that uses a few new twists, such as multi-stage features, connections that skip layers to integrate global shape information with local distinctive motif information.