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Current state-of-the-art deep learning systems for visual object recognition and detection use purely supervised training with regularization such as dropout to avoid overfitting.
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Analyzing the effectiveness and applicability of co-training
Nigam, Kamal and Ghani, Rayid · 2000
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Text classification from labeled and unlabeled documents using em
Nigam, Kamal, McCallum, Andrew Kachites, Thrun, Sebastian, and Mitchell, Tom · 2000
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Training products of experts by minimizing contrastive divergence
Hinton, Geoffrey E · 2002
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Understanding the yarowsky algorithm
Abney, Steven · 2004
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Semi-supervised learning by entropy minimization
Grandvalet, Yves and Bengio, Yoshua · 2005
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Semi-supervised self-training of object detection models
Rosenberg, Chuck, Hebert, Martial, and Schneiderman, Henry · 2005
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Semi-supervised learning literature survey
Zhu, Xiaojin · 2005
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9 entropy regularization
Grandvalet, Yves and Bengio, Yoshua · 2006
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Classification using discriminative restricted boltzmann machines
Larochelle, Hugo and Bengio, Yoshua · 2008
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Restricted boltzmann machine with hidden multinomial output unit
Hinton, Geoffrey E and Mnih, Volodymyr · 2009
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Deep boltzmann machines
Salakhutdinov, Ruslan and Hinton, Geoffrey E · 2009
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Susskind, Josh M, Anderson, Adam K, and Hinton, Geoffrey E · 2010
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The importance of encoding versus training with sparse coding and vector quantization
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Rich feature hierarchies for accurate object detection and semantic segmentation
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Multi-prediction deep boltzmann machines
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
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Sermanet, Pierre, Eigen, David, Zhang, Xiang, Mathieu, Michaël, Fergus, Rob, and LeCun, Yann · 2013
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Visualizing and understanding convolutional neural networks
Zeiler, Matthew D and Fergus, Rob · 2013
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Improving neural networks by preventing co-adaptation of feature detectors
Hinton, Geoffrey E, Srivastava, Nitish, Krizhevsky, Alex, Sutskever, Ilya, and Salakhutdinov, Ruslan R · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geoffrey E · 2012
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Bengio, Yoshua and Thibodeau-Laufer, Eric · 2013
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Enriching visual knowledge bases via object discovery and segmentation
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Scalable Object Detection Using Deep Neural Networks
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Deepid-net: multi-stage and deformable deep convolutional neural networks for object detection
Ouyang, Wanli, Luo, Ping, Zeng, Xingyu, Qiu, Shi, Tian, Yonglong, Li, Hongsheng, Yang, Shuo, Wang, Zhe, Xiong, Yuanjun, Qian, Chen, et al · 2014
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Learning to disentangle factors of variation with manifold interaction
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ImageNet Large Scale Visual Recognition Challenge, 2014
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