Learning Feature Hierarchies for Object Recognition
暂无分享,去创建一个
[1] Guillermo Sapiro,et al. Discriminative learned dictionaries for local image analysis , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[2] Pietro Perona,et al. Integral Channel Features , 2009, BMVC.
[3] Pietro Perona,et al. Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.
[4] Marc'Aurelio Ranzato,et al. Efficient Learning of Sparse Representations with an Energy-Based Model , 2006, NIPS.
[5] Stéphane Mallat,et al. Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..
[6] Jean Ponce,et al. Learning mid-level features for recognition , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[7] M. Elad,et al. $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.
[8] G LoweDavid,et al. Distinctive Image Features from Scale-Invariant Keypoints , 2004 .
[9] David J. C. MacKay,et al. Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.
[10] Geoffrey E. Hinton,et al. Learning Sparse Topographic Representations with Products of Student-t Distributions , 2002, NIPS.
[11] Bhaskar D. Rao,et al. Perspectives on Sparse Bayesian Learning , 2003, NIPS.
[12] Michael Elad,et al. K-SVD and its non-negative variant for dictionary design , 2005, SPIE Optics + Photonics.
[13] Antonio Torralba,et al. Ieee Transactions on Pattern Analysis and Machine Intelligence 1 80 Million Tiny Images: a Large Dataset for Non-parametric Object and Scene Recognition , 2022 .
[14] Eero P. Simoncelli,et al. Natural signal statistics and sensory gain control , 2001, Nature Neuroscience.
[15] Marc Teboulle,et al. A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..
[16] A. Hyvärinen,et al. Complex cell pooling and the statistics of natural images , 2007, Network.
[17] Gökhan BakIr,et al. Predicting Structured Data , 2008 .
[18] Richard G. Baraniuk,et al. Sparse Coding via Thresholding and Local Competition in Neural Circuits , 2008, Neural Computation.
[19] Yann LeCun,et al. What is the best multi-stage architecture for object recognition? , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[20] David G. Lowe,et al. Multiclass Object Recognition with Sparse, Localized Features , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[21] R. Tibshirani,et al. Least angle regression , 2004, math/0406456.
[22] I. Daubechies,et al. An iterative thresholding algorithm for linear inverse problems with a sparsity constraint , 2003, math/0307152.
[23] Yoshua Bengio,et al. Exploring Strategies for Training Deep Neural Networks , 2009, J. Mach. Learn. Res..
[24] Marc'Aurelio Ranzato,et al. Sparse Feature Learning for Deep Belief Networks , 2007, NIPS.
[25] Nicolas Pinto,et al. Why is Real-World Visual Object Recognition Hard? , 2008, PLoS Comput. Biol..
[26] Florian Steinke,et al. Bayesian Inference and Optimal Design in the Sparse Linear Model , 2007, AISTATS.
[27] Jitendra Malik,et al. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.
[28] Eero P. Simoncelli,et al. Natural image statistics and divisive normalization: Modeling nonlinearity and adaptation in cortical neurons , 2002 .
[29] David J. Field,et al. Sparse coding with an overcomplete basis set: A strategy employed by V1? , 1997, Vision Research.
[30] Quoc V. Le,et al. Measuring Invariances in Deep Networks , 2009, NIPS.
[31] Michael Elad,et al. Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ1 minimization , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[32] Aapo Hyvärinen,et al. A two-layer sparse coding model learns simple and complex cell receptive fields and topography from natural images , 2001, Vision Research.
[33] Yihong Gong,et al. Training Hierarchical Feed-Forward Visual Recognition Models Using Transfer Learning from Pseudo-Tasks , 2008, ECCV.
[34] Bill Triggs,et al. Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[35] Mark A. Girolami,et al. A Variational Method for Learning Sparse and Overcomplete Representations , 2001, Neural Computation.
[36] Cordelia Schmid,et al. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[37] Pierre Comon,et al. Independent component analysis, A new concept? , 1994, Signal Process..
[38] Y. Nesterov. Gradient methods for minimizing composite objective function , 2007 .
[39] Bernt Schiele,et al. New features and insights for pedestrian detection , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[40] R. Fergus,et al. Learning invariant features through topographic filter maps , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[41] David A. McAllester,et al. Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[42] Yann LeCun,et al. Large-scale Learning with SVM and Convolutional for Generic Object Categorization , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[43] Honglak Lee,et al. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations , 2009, ICML '09.
[44] Teuvo Kohonen,et al. Emergence of invariant-feature detectors in the adaptive-subspace self-organizing map , 1996, Biological Cybernetics.
[45] Y. LeCun,et al. Learning methods for generic object recognition with invariance to pose and lighting , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..
[46] Aapo Hyvärinen,et al. Emergence of Phase- and Shift-Invariant Features by Decomposition of Natural Images into Independent Feature Subspaces , 2000, Neural Computation.
[47] S. Osher,et al. Coordinate descent optimization for l 1 minimization with application to compressed sensing; a greedy algorithm , 2009 .
[48] Thomas Serre,et al. Object recognition with features inspired by visual cortex , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[49] Jason Weston,et al. Deep learning via semi-supervised embedding , 2008, ICML '08.
[50] Thomas Hofmann,et al. Greedy Layer-Wise Training of Deep Networks , 2007 .
[51] Rajat Raina,et al. Efficient sparse coding algorithms , 2006, NIPS.
[52] Geoffrey E. Hinton,et al. Topographic Product Models Applied to Natural Scene Statistics , 2006, Neural Computation.
[53] Trevor Darrell,et al. The pyramid match kernel: discriminative classification with sets of image features , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[54] Graham W. Taylor,et al. Deconvolutional networks , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[55] Marc'Aurelio Ranzato,et al. Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[56] Jason Weston,et al. A unified architecture for natural language processing: deep neural networks with multitask learning , 2008, ICML '08.
[57] Yann LeCun,et al. Learning Fast Approximations of Sparse Coding , 2010, ICML.
[58] Joseph F. Murray,et al. Learning Sparse Overcomplete Codes for Images , 2006, J. VLSI Signal Process..
[59] Michael A. Saunders,et al. Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..
[60] D. Field,et al. Natural image statistics and efficient coding. , 1996, Network.
[61] Bruno A. Olshausen,et al. Sparse Coding Of Time-Varying Natural Images , 2010 .
[62] Yanjun Qi,et al. Semi-Supervised Sequence Labeling with Self-Learned Features , 2009, 2009 Ninth IEEE International Conference on Data Mining.
[63] A. Bruckstein,et al. K-SVD : An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation , 2005 .
[64] Yihong Gong,et al. Linear spatial pyramid matching using sparse coding for image classification , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[65] Pietro Perona,et al. The Fastest Pedestrian Detector in the West , 2010, BMVC.
[66] M. Yuan,et al. Model selection and estimation in regression with grouped variables , 2006 .
[67] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[68] Fu Jie Huang,et al. A Tutorial on Energy-Based Learning , 2006 .
[69] Y-Lan Boureau,et al. Learning Convolutional Feature Hierarchies for Visual Recognition , 2010, NIPS.
[70] Klaus-Robert Müller,et al. Efficient BackProp , 2012, Neural Networks: Tricks of the Trade.
[71] Eero P. Simoncelli,et al. Nonlinear image representation using divisive normalization , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[72] Guillermo Sapiro,et al. Online dictionary learning for sparse coding , 2009, ICML '09.
[73] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.