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• W2157989183 abstract "In visual recognition problems, the common data distribution mismatches between training and testing make domain adaptation essential. However, image data is difficult to manually divide into the discrete domains required by adaptation algorithms, and the standard practice of equating datasets with domains is a weak proxy for all the real conditions that alter the statistics in complex ways (lighting, pose, background, resolution, etc.) We propose an approach to automatically discover latent domains in image or video datasets. Our formulation imposes two key properties on domains: maximum distinctiveness and maximum learnability. By maximum distinctiveness, we require the underlying distributions of the identified domains to be different from each other to the maximum extent; by maximum learnability, we ensure that a strong discriminative model can be learned from the domain. We devise a nonparametric formulation and efficient optimization procedure that can successfully discover domains among both training and test data. We extensively evaluate our approach on object recognition and human activity recognition tasks." @default.
• W2157989183 created "2016-06-24" @default.
• W2157989183 creator A5012765543 @default.
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• W2157989183 date "2013-12-05" @default.
• W2157989183 modified "2023-09-29" @default.
• W2157989183 title "Reshaping Visual Datasets for Domain Adaptation" @default.
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• W2157989183 hasPublicationYear "2013" @default.
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