Fully semisupervised framework for visual domain adaptation

Depeng Gao; Jiafeng Liu; Rui Wu; Xianglong Tang

doi:10.1117/1.JEI.28.1.013040

18 February 2019 Fully semisupervised framework for visual domain adaptation

Depeng Gao, Jiafeng Liu, Rui Wu, Xianglong Tang

Author Affiliations +

Journal of Electronic Imaging, Vol. 28, Issue 1, 013040 (February 2019). https://doi.org/10.1117/1.JEI.28.1.013040

Abstract

Unsupervised domain adaptation aims to utilize knowledge from a source domain to improve learning in a target domain in cases in which abundant labeled samples are available in the source domain, but no labels exist in the target domain. The two domains have the same feature space and label space but different distributions. Our study proposes a semisupervised framework in which labeled samples in the source domain and unlabeled target samples are fully utilized to learn a better classifier. The framework contains two stages: semisupervised feature learning and semisupervised classifier learning. In the first stage, a transformation is learned using labeled source samples and unlabeled target samples to map these data into a representation. In the second stage, a classifier is learned using all samples in the source and target domains of the representation. Furthermore, we propose a semisupervised feature learning approach (i.e., cross-domain discriminative analysis) to learn the transformation in the first stage by reducing the distribution discrepancies between domains and preserving discriminative information in the original data. In our experiments, image classification tasks were conducted using several well-known cross-domain datasets. The proposed method outperformed the state-of-the-art methods in most cases.

Citation Download Citation

Depeng Gao, Jiafeng Liu, Rui Wu, and Xianglong Tang "Fully semisupervised framework for visual domain adaptation," Journal of Electronic Imaging 28(1), 013040 (18 February 2019). https://doi.org/10.1117/1.JEI.28.1.013040

Received: 8 October 2018; Accepted: 28 January 2019; Published: 18 February 2019

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KEYWORDS

Visualization

Solid state lighting

Cameras

Detection and tracking algorithms

Facial recognition systems

Distance measurement

Image classification

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