A neural network model for selective attention in visual pattern recognition
Biological Cybernetics
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Object Recognition with Features Inspired by Visual Cortex
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Multiclass Object Recognition with Sparse, Localized Features
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Large-scale Learning with SVM and Convolutional for Generic Object Categorization
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Rapid Biologically-Inspired Scene Classification Using Features Shared with Visual Attention
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Neural Networks for Image Interpretation (Lecture Notes in Computer Science)
Hierarchical Neural Networks for Image Interpretation (Lecture Notes in Computer Science)
Computer Vision and Image Understanding
Synergistic Face Detection and Pose Estimation with Energy-Based Models
The Journal of Machine Learning Research
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Learning methods for generic object recognition with invariance to pose and lighting
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Stacked convolutional auto-encoders for hierarchical feature extraction
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
On fast deep nets for AGI vision
AGI'11 Proceedings of the 4th international conference on Artificial general intelligence
Flexible, high performance convolutional neural networks for image classification
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Effects of architecture choices on sparse coding in speech recognition
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Multiscale convolutional neural networks for vision: based classification of cells
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
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A common practice to gain invariant features in object recognition models is to aggregate multiple low-level features over a small neighborhood. However, the differences between those models makes a comparison of the properties of different aggregation functions hard. Our aim is to gain insight into different functions by directly comparing them on a fixed architecture for several common object recognition tasks. Empirical results show that a maximum pooling operation significantly outperforms subsampling operations. Despite their shift-invariant properties, overlapping pooling windows are no significant improvement over nonoverlapping pooling windows. By applying this knowledge, we achieve state-of-the-art error rates of 4.57% on the NORB normalized-uniform dataset and 5.6% on the NORB jittered-cluttered dataset.