2013
CVPR
CVPR 2013
Adding Unlabeled Samples to Categories by Learned Attributes
Abstract
We propose a method to expand the visual coverage of training sets that consist of a small number of labeled examples using learned attributes. Our optimization formulation discovers category specific attributes as well as the images that have high confidence in terms of the attributes. In addition, we propose a method to stably capture example-specific attributes for a small sized training set. Our method adds images to a category from a large unlabeled image pool, and leads to significant improvement in category recognition accuracy evaluated on a large-scale dataset, ImageNet.
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Conference Pioneer
— CVPR 2013
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Interdisciplinary Bridge
— Computer Vision and Deep Learning and Machine Learning
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Trend Setter
— Semi-Supervised Learning
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Keyword Pioneer
— category recognition
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Core Methods > Classification
Machine Learning > Learning Types > Semi-Supervised Learning
Machine Learning > Learning Types > Unsupervised Learning
Machine Learning > Learning Types > Transfer Learning
Deep Learning > Learning Types > Representation Learning
Deep Learning > Learning Types > Semi-Supervised Learning
Computer Vision > Analysis > Image Classification