Deep Epitomic Convolutional Neural Networks

Keywords

MNIST database, Convolutional neural network, Pooling, Computer science, Artificial intelligence

References

• Return of the Devil in the Details: Delving Deep into Convolutional NetsKen Chatfield, Karen Simonyan, Andrea Vedaldi — 2014
• Modeling Image Patches with a Generic Dictionary of Mini-EpitomesGeorge Papandreou, Liang-Chieh Chen, Alan Yuille — 2014
• Sparse and Redundant Modeling of Image Content Using an Image-Signature-DictionaryMichal Aharon, Michael Elad — 2008
• CNN Features Off-the-Shelf: An Astounding Baseline for RecognitionAli Sharif Razavian, Hossein Azizpour, Josephine Sullivan — 2014
• Rich Feature Hierarchies for Accurate Object Detection and Semantic SegmentationRoss Girshick, Jeff Donahue, Trevor Darrell — 2013
• ImageNet: A large-scale hierarchical image databaseJia Deng, Wei Dong, Richard Socher — 2009
• Gradient-based learning applied to document recognitionYann LeCun, Léon Bottou, Yoshua Bengio — 1998
• Building high-level features using large scale unsupervised learningQuoc V. Le — 2011
• Convolutional deep belief networks for scalable unsupervised learning of hierarchical representationsHonglak Lee, Roger Grosse, Rajesh Ranganath — 2009
• Epitomic analysis of appearance and shapeJojic, Frey, K. Kannan — 2003
• Learning invariant features through topographic filter mapsKoray Kavukcuoglu, Marc’Aurelio Ranzato, Rob Fergus — 2009
• Topographic Product Models Applied to Natural Scene StatisticsSimon Osindero, Max Welling, Geoffrey E. Hinton — 2006
• Hierarchical models of object recognition in cortexMaximilian Riesenhuber, Tomaso Poggio — 1999
• Distinctive Image Features from Scale-Invariant KeypointsDavid Lowe — 2004
• LIBSVM: A library for support vector machinesChih-Chung Chang, Chih‐Jen Lin — 2011
• Joint Deep Learning for Pedestrian DetectionWanli Ouyang, Xiaogang Wang — 2013
• ImageNet classification with deep convolutional neural networksAlex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton — 2012
• Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object CategoriesLi Fei-Fei, Rob Fergus, Pietro Perona — 2004
• Distinctive Image Features from Scale-Invariant Keypoints Abstract by Matthijs Dorst Based on the paper byMatthijs C. Dorst — 2011
• Deconvolutional networksMatthew D. Zeiler, Dilip Krishnan, Graham W. Taylor — 2010

Cited by

• Untangling Local and Global Deformations in Deep Convolutional Networks for Image Classification and Sliding Window DetectionGeorge Papandreou, Iasonas Kokkinos, Pierre-André Savalle — 2014
• Modeling local and global deformations in Deep Learning: Epitomic convolution, Multiple Instance Learning, and sliding window detectionGeorge Papandreou, Iasonas Kokkinos, Pierre-André Savalle — 2015
• ImageNet Large Scale Visual Recognition ChallengeOlga Russakovsky, Jia Deng, Hao Su — 2014
• Good Practice in CNN Feature TransferLiang Zheng, Yali Zhao, Shengjin Wang — 2016
• Efficient learning of local image descriptorsVassileios Balntas — 2016
• Efficient Convolutional Network Learning Using Parametric Log Based Dual-Tree Wavelet ScatterNetAmarjot Singh, Nick Kingsbury — 2017
• AHCNet: An Application of Attention Mechanism and Hybrid Connection for Liver Tumor Segmentation in CT VolumesHuiyan Jiang, Tianyu Shi, Zhiqi Bai — 2019

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