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When Unsupervised Domain Adaptation Meets Tensor Representations
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README.md

Tensor-Aligned Invariant Subspace Learning

When Unsupervised Domain Adaptation Meets Tensor Representations

Proc. IEEE International Conference on Computer Vision (ICCV), 2017

By Hao Lu1, Lei Zhang2, Zhiguo Cao1, Wei Wei2, Ke Xian1, Chunhua Shen3, Anton van den Hengel3

1Huazhong University of Science and Technology, China

2Northwestern Polytechnical University, China

3The University of Adelaide, Australia

Introduction

This repository contains the implimentation of Naive Tensor Subspace Learning (NTSL) and Tensor-Aligned Invariant Subspace Learning (TAISL) proposed in our ICCV17 paper.

Prerequisites

  1. Matlab is required. This repository has been tested on 64-bit Mac OS X Matlab2016a. The code should also be compatible with Windows 10.
  2. LibLinear toolbox at: https://www.csie.ntu.edu.tw/~cjlin/liblinear/. Please remember to install it following the instruction on the website, especially for Windows and Ubuntun users.
  3. Tensor Toolbox at: http://www.sandia.gov/~tgkolda/TensorToolbox/index-2.6.html.
  4. Matlab code for optimization with orthogonality constraints at: http://optman.blogs.rice.edu.

For your convenience, these toolboxs have already been included in this repository. Please remember to cite corresponding papers/softwares if you use these codes.

Usage

  1. run demo.m for a demonstration for the domain adaptation task of D->C.

Citation

If you use our codes in your research, please cite:

@inproceedings{Hao2017,
	author = {Hao Lu and Lei Zhang and Zhiguo Cao and Wei Wei and Ke Xian and Chunhua Shen and Anton van den Hengel},
	title = {When Unsupervised Domain Adaptation Meets Tensor Representations},
	booktitle = {Proc. IEEE International Conference on Computer Vision (ICCV)},
	year = {2017}
}
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