Handwritten digit recognition with a back-propagation network
Advances in neural information processing systems 2
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
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
Topographic Product Models Applied to Natural Scene Statistics
Neural Computation
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
A fast Iterative Shrinkage-Thresholding Algorithm with application to wavelet-based image deblurring
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Convolutional learning of spatio-temporal features
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
The Journal of Machine Learning Research
Natural Language Processing (Almost) from Scratch
The Journal of Machine Learning Research
Convex and Network Flow Optimization for Structured Sparsity
The Journal of Machine Learning Research
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition
IEEE Transactions on Audio, Speech, and Language Processing
Ask the locals: Multi-way local pooling for image recognition
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Feature learning and deep architectures: new directions for music informatics
Journal of Intelligent Information Systems
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Fast visual recognition in the mammalian cortex seems to be a hierarchical process by which the representation of the visual world is transformed in multiple stages from low-level retinotopic features to high-level, global and invariant features, and to object categories. Every single step in this hierarchy seems to be subject to learning. How does the visual cortex learn such hierarchical representations by just looking at the world? How could computers learn such representations from data? Computer vision models that are weakly inspired by the visual cortex will be described. A number of unsupervised learning algorithms to train these models will be presented, which are based on the sparse auto-encoder concept. The effectiveness of these algorithms for learning invariant feature hierarchies will be demonstrated with a number of practical tasks such as scene parsing, pedestrian detection, and object classification.