The nature of statistical learning theory
The nature of statistical learning theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised learning
Slow feature analysis: unsupervised learning of invariances
Neural Computation
A Hybrid Face Recognition Method using Markov Random Fields
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Learning a Similarity Metric Discriminatively, with Application to Face Verification
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
ICML '05 Proceedings of the 22nd international conference on Machine learning
Semi-Supervised Learning (Adaptive Computation and Machine Learning)
Semi-Supervised Learning (Adaptive Computation and Machine Learning)
Synergistic Face Detection and Pose Estimation with Energy-Based Models
The Journal of Machine Learning Research
Deep learning via semi-supervised embedding
Proceedings of the 25th international conference on Machine learning
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Learning Deep Architectures for AI
Foundations and Trends® in Machine Learning
Why Does Unsupervised Pre-training Help Deep Learning?
The Journal of Machine Learning Research
Research frontier: deep machine learning--a new frontier in artificial intelligence research
IEEE Computational Intelligence Magazine
Discriminative deep belief networks for visual data classification
Pattern Recognition
On the expressive power of deep architectures
ALT'11 Proceedings of the 22nd international conference on Algorithmic learning theory
Semi-supervised learning using greedy max-cut
The Journal of Machine Learning Research
Hierarchical spatiotemporal feature extraction using recurrent online clustering
Pattern Recognition Letters
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This work proposes a learning method for deep architectures that takes advantage of sequential data, in particular from the temporal coherence that naturally exists in unlabeled video recordings. That is, two successive frames are likely to contain the same object or objects. This coherence is used as a supervisory signal over the unlabeled data, and is used to improve the performance on a supervised task of interest. We demonstrate the effectiveness of this method on some pose invariant object and face recognition tasks.