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Enhancing inter-class representation with a new global center loss

Title
Enhancing inter-class representation with a new global center loss
Authors
Kang, Myeong K.Lee, In H.Lee, Eun H.Baek, Sang Y.Lee, Sang Cheol
DGIST Authors
Lee, Sang Cheol
Issue Date
2017-12-28
Citation
2017 International Conference on Industrial Design Engineering, ICIDE 2017, 112-120
Type
Conference
ISBN
9781450348669
Abstract
The challenge of training convolutional neural networks (CNNs) is mapping a set of high dimensional low data to a lower dimensional feature point on the manifold. However, the dimensionality reduced feature from CNN always suffers from verifying a new unseen class, such as measuring a computably meaningful distance on the dimensionality distorted space. The mapped feature on the lower dimension is formed by interrelated with training dataset and measure of penalization of learning. These methods can boost performance by training a large dataset or applying extra penalizations, which help forming feature more discriminatively. The discriminatively learned feature has a balanced inter-class distance and a reduced intra-class variation. In this paper, we propose a new loss function so called global center loss to extract more meaningful distance on feature space. The method leads to a sufficient inter-class variation which helps forming feature more discriminatively. The insufficient enforcement of negative-log likelihood or local center loss can be complementarily enhanced by utilizing the proposed global center method which is a valuable joint supervisory signal. © 2017 Association for Computing Machinery.
URI
http://hdl.handle.net/20.500.11750/9049
DOI
10.1145/3178264.3178279
Publisher
Association for Computing Machinery
Related Researcher
Files:
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Collection:
Convergence Research Center for Future Automotive Technology2. Conference Papers


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