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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.


Improving predictors via combination across diverse task categories

DC Field Value Language Kim, Kwang In ko 2021-07-08T08:42:54Z - 2021-06-21 ko 2021-07-20 ko
dc.identifier.citation IEEE International Conference on Machine Learning ko
dc.identifier.uri -
dc.description.abstract Predictor combination is the problem of improving a task predictor using predictors of other tasks when the forms of individual predictors are unknown. Previous work approached this problem by nonparametrically assessing predictor relationships based on their joint evaluations on a shared sample. This limits their application to cases where all predictors are defined on the same task category, e.g. all predictors estimate attributes of shoes. We present a new predictor combination algorithm that overcomes this limitation. Our algorithm aligns the heterogeneous domains of different predictors in a shared latent space to facilitate comparisons of predictors independently of the domains on which they are originally defined. We facilitate this by a new data alignment scheme that matches data distributions across task categories. Based on visual attribute ranking experiments on datasets that span diverse task categories (e.g. shoes and animals), we demonstrate that our approach often significantly improves the performances of the initial predictors. ko
dc.language 영어 ko
dc.publisher International Conference on Machine Learning ko
dc.title Improving predictors via combination across diverse task categories ko
dc.type CONFERENCE ko
dc.type.rims CONF ko
dc.identifier.url ko
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