基本信息
姓名 李晓
硕导
博士学科：计算机应用技术
硕士学科：计算机应用技术　
工作单位：计算机学院

联系方式
通信地址：
电子邮箱：xiaoli@xidian.edu.cn
办公电话：
办公地点：主楼四区303


个人简介
李晓，西安电子科技大学博士毕业。研究方向包括机器学习，深度学习和计算机视觉。机器学习方面包括小样本学习，零样本学习，迁移学习及其应用等。深度学习方面包括生成式对抗网络，卷积神经网络等。计算机视觉方面包括图像分类，物体识别，RGB-D图像分类和行为识别等。已经发表了多篇高质量的SCI论文，发表在Knowledge-Based Systems，Pattern Recognition，Neurocomputing等国际期刊上。主持和参与多项科研项目，包括青年科学基金项目和博士后面上项目等。
1 Xiao Li, Min Fang, Haikun Li. Learning domain invariant unseen features for generalized zero-shot classification[J]. Knowledge-Based Systems, 2020:106378.
2 Xiao Li, Min Fang, Jinqiao Wu. Zero-shot classification by transferring knowledge and preserving data structure[J]. Neurocomputing, 2017, 238: 76-83.
3 Xiao Li, Min Fang, Ju-Jie Zhang, Jinqiao Wu. Learning Coupled Classifiers with RGB images for RGB-D object recognition [J]. Pattern Recognition, 2017, 61: 433-446.
4 Xiao Li, Min Fang, Dazheng Feng, Haikun Li, Jinqiao Wu. Learning unseen visual prototypes for zero-shot classification [J]. Knowledge-Based Systems, 2018, 160: 176-187.
5 Xiao Li, Min Fang, Ju-Jie Zhang, Jinqiao Wu. Domain adaptation from RGB-D to RGB images[J]. Signal Processing, 2017, 131: 27-35.

主要研究方向
1.机器学习：零样本学习，小样本学习，迁移学习，域自适应等
2.深度学习：生成式对抗网络，变分自编码器，卷积神经网络等
3.计算机视觉：图像分类，物体识别，行为识别等

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基本信息
姓名 李晓
硕导
博士学科：计算机应用技术
硕士学科：计算机应用技术　
工作单位：计算机学院

联系方式
通信地址：
电子邮箱：xiaoli@xidian.edu.cn
办公电话：
办公地点：主楼四区303


个人简介
李晓，西安电子科技大学博士毕业。研究方向包括机器学习，深度学习和计算机视觉。机器学习方面包括小样本学习，零样本学习，迁移学习及其应用等。深度学习方面包括生成式对抗网络，卷积神经网络等。计算机视觉方面包括图像分类，物体识别，RGB-D图像分类和行为识别等。已经发表了多篇高质量的SCI论文，发表在Knowledge-Based Systems，Pattern Recognition，Neurocomputing等国际期刊上。主持和参与多项科研项目，包括青年科学基金项目和博士后面上项目等。
1 Xiao Li, Min Fang, Haikun Li. Learning domain invariant unseen features for generalized zero-shot classification[J]. Knowledge-Based Systems, 2020:106378.
2 Xiao Li, Min Fang, Jinqiao Wu. Zero-shot classification by transferring knowledge and preserving data structure[J]. Neurocomputing, 2017, 238: 76-83.
3 Xiao Li, Min Fang, Ju-Jie Zhang, Jinqiao Wu. Learning Coupled Classifiers with RGB images for RGB-D object recognition [J]. Pattern Recognition, 2017, 61: 433-446.
4 Xiao Li, Min Fang, Dazheng Feng, Haikun Li, Jinqiao Wu. Learning unseen visual prototypes for zero-shot classification [J]. Knowledge-Based Systems, 2018, 160: 176-187.
5 Xiao Li, Min Fang, Ju-Jie Zhang, Jinqiao Wu. Domain adaptation from RGB-D to RGB images[J]. Signal Processing, 2017, 131: 27-35.

主要研究方向
1.机器学习：零样本学习，小样本学习，迁移学习，域自适应等
2.深度学习：生成式对抗网络，变分自编码器，卷积神经网络等
3.计算机视觉：图像分类，物体识别，行为识别等

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科学研究
目前研究团队承担的科研项目：

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学术论文
[1] Xiao Li, Min Fang, Dazheng Feng, Haikun Li, Jinqiao Wu. Prototype adjustment for zero shot classification [J]. Signal Processing: Image Communication, 2019.
[2] Xiao Li, Min Fang, Dazheng Feng, Haikun Li, Jinqiao Wu. Zero shot learning by partial transfer from source domain with L2,1 norm constraint[J]. Journal of Visual Communication and Image Representation, 2019, 58: 701-711.
[3] Xiao Li, Min Fang, Dazheng Feng, Haikun Li, Jinqiao Wu. Learning unseen visual prototypes for zero-shot classification [J]. Knowledge-Based Systems, 2018, 160: 176-187.
[4] Xiao Li, Min Fang, Jinqiao Wu. Zero-shot classification by transferring knowledge and preserving data structure[J]. Neurocomputing, 2017, 238: 76-83.
[5] Xiao Li, Min Fang, Ju-Jie Zhang, Jinqiao Wu. Learning Coupled Classifiers with RGB images for RGB-D object recognition [J]. Pattern Recognition, 2017, 61: 433-446.
[6] Xiao Li, Min Fang, Ju-Jie Zhang, Jinqiao Wu. Domain adaptation from RGB-D to RGB images[J]. Signal Processing, 2017, 131: 27-35.
[7] Xiao Li, Min Fang, Ju-Jie Zhang, Jinqiao Wu. Sample selection for visual domain adaptation via sparse coding[J]. Signal Processing: Image Communication, 2016, 44: 92-100.
[8] Xiao Li, Min Fang, Ju-Jie Zhang. Projected transfer sparse coding for cross domain image representation[J]. Journal of Visual Communication and Image Representation, 2015, 33: 265-272
[9] Xiao Li, Min Fang, Hongchun Wang, Ju-Jie Zhang. Supervised transfer kernel sparse coding for image classification[J]. Pattern Recognition Letters, 2015, 68: 27-33.
[10] Xiao Li, Min Fang, Jinqiao Wu, Liang He, Xian Tian. Image classification by semisupervised sparse coding with confident unlabeled samples[J]. Journal of Electronic Imaging, 2017, 26(5): 053013.
[11] Zhang J J, Fang M, Li X. Clustered Intrinsic Label Correlations for Multi-label Classification[J]. Expert Systems with Applications, 2017, 81: 134-146.
[12] Zhang J J, Fang M, Wu J Q, Li, X. Robust label compression for multi-label classification[J]. Knowledge-Based Systems, 2016, 107: 32-42.
[13] Zhang J J, Fang M, Wang H, Li X. Dependence maximization based label space dimension reduction for multi-label classification[J]. Engineering Applications of Artificial Intelligence, 2015, 45: 453-463.
[14] Zhang J J, Fang M, Li X. Multi-label learning with discriminative features for each label[J]. Neurocomputing, 2015, 154: 305-316.
[15] Fang M, Guo Y, Zhang X, Li X. Multi-source transfer learning based on label shared subspace[J]. Pattern Recognition Letters, 2015, 51: 101-106.

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荣誉获奖
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科研团队
团队教师




博士研究生
硕士研究生

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课程教学
目前本人承担的教学任务：
操作系统
智能感知与决策
认知计算与决策技术实验

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招生要求
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关于研究生招生的信息：
编程语言：Python、Matlab、Java等
机器学习、深度学习基础
良好的外语、数学水平
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Research
目前研究团队承担的科研项目：

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Papers
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Honors
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Team
团队教师




博士研究生
硕士研究生

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Admission
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