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Kim, Kwang In
Machine Learning and Vision Lab
Research Interests
  • Neural networks, semi-supervised learning; unsupervised learning; learning on Riemannian manifolds and graph-structured data; human body pose estimation; human hand pose estimation; image and video enhancement.

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Testing using privileged information by adapting features with statistical dependence

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Title
Testing using privileged information by adapting features with statistical dependence
Author
Kim, Kwang InTompkin, James
Issue Date
2021-10-11
Publisher
IEEE
Citation
IEEE International Conference on Computer Vision, pp.9405 - 9413
Abstract
Given an imperfect predictor, we exploit additional features at test time to improve the predictions made, without retraining and without knowledge of the prediction function. This scenario arises if training labels or data are proprietary, restricted, or no longer available, or if training itself is prohibitively expensive. We assume that the additional features are useful if they exhibit strong statistical dependence to the underlying perfect predictor. Then, we empirically estimate and strengthen the statistical dependence between the initial noisy predictor and the additional features via manifold denoising. As an example, we show that this approach leads to improvement in real-world visual attribute ranking.
URI
https://scholarworks.unist.ac.kr/handle/201301/54601
URL
https://openaccess.thecvf.com/content/ICCV2021/papers/Kim_Testing_Using_Privileged_Information_by_Adapting_Features_With_Statistical_Dependence_ICCV_2021_paper.pdf
DOI
10.1109/ICCV48922.2021.00927
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