Unsupervised Deep Feature Transfer for Low Resolution Image Classification

Yuanwei Wu, Ziming Zhang, Guanghui Wang; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 0-0

Abstract


In this paper, we propose a simple while effective unsupervised deep feature transfer algorithm for low resolution image classification. No fine-tuning on convenet filters is required in our method. We use pre-trained convenet to extract features for both high-and low-resolution images, and then feed them into a two-layer feature transfer network for knowledge transfer. A SVM classifier is learned directly using these transferred low resolution features. Our network can be embedded into the state-of-the-art deep neural networks as a plug-in feature enhancement module. It preserves data structures in feature space for high resolution images, and transfers the distinguishing features from a well-structured source domain (high resolution features space) to a not well-organized target domain (low resolution features space). Extensive experiments on VOC2007 test set show that the proposed method achieves significant improvements over the baseline of using feature extraction.

Related Material


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[bibtex]
@InProceedings{Wu_2019_ICCV,
author = {Wu, Yuanwei and Zhang, Ziming and Wang, Guanghui},
title = {Unsupervised Deep Feature Transfer for Low Resolution Image Classification},
booktitle = {The IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2019}
}