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Learning Convolutional Nonlinear Features for K Nearest Neighbor Image Classification
2014
2014 22nd International Conference on Pattern Recognition
Learning low-dimensional feature representations is a crucial task in machine learning and computer vision. Recently the impressive breakthrough in general object recognition made by large scale convolutional networks shows that convolutional networks are able to extract discriminative hierarchical features in large scale object classification task. However, for vision tasks other than end-to-end classification, such as K Nearest Neighbor classification, the learned intermediate features are
doi:10.1109/icpr.2014.746
dblp:conf/icpr/RenYZH14
fatcat:7ozsr5o6jjeype4xq2gmo3btoq