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摘要翻译：
人类活动识别在人们的日常生活中起着重要的作用。然而，获取足够的标记活动数据往往既昂贵又耗时。为了解决这一问题，迁移学习利用源域的标记样本来标注标记很少或没有标记的目标域。不幸的是，当有几个源域可用时，很难选择合适的源域进行传输。正确的源域意味着它与目标域具有最相似的性质，因此它们的相似度较高，有利于迁移学习。选择合适的源域有助于提高算法的性能，防止负迁移。本文提出了一种有效的无监督活动识别源选择算法(USSAR)。USSAR能够从可用域列表中选择最相似的$K$源域。在此基础上，我们提出了一种有效的传递神经网络来实现活动识别中的知识传递(TNNAR)。TNNAR能够捕捉知识转移过程中活动之间的时间和空间关系。在三个公共活动识别数据集上的实验表明:1)USSAR算法在选择最佳源域方面是有效的；2)TNNAR方法在进行活动知识转移时具有较高的准确率。
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英文标题：
《Deep Transfer Learning for Cross-domain Activity Recognition》
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作者：
Jindong Wang, Vincent W. Zheng, Yiqiang Chen, Meiyu Huang
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最新提交年份：
2018
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分类信息：
一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Image and Video Processing 图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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一级分类：Statistics 统计学
二级分类：Machine Learning 机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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英文摘要：
Human activity recognition plays an important role in people's daily life. However, it is often expensive and time-consuming to acquire sufficient labeled activity data. To solve this problem, transfer learning leverages the labeled samples from the source domain to annotate the target domain which has few or none labels. Unfortunately, when there are several source domains available, it is difficult to select the right source domains for transfer. The right source domain means that it has the most similar properties with the target domain, thus their similarity is higher, which can facilitate transfer learning. Choosing the right source domain helps the algorithm perform well and prevents the negative transfer. In this paper, we propose an effective Unsupervised Source Selection algorithm for Activity Recognition (USSAR). USSAR is able to select the most similar $K$ source domains from a list of available domains. After this, we propose an effective Transfer Neural Network to perform knowledge transfer for Activity Recognition (TNNAR). TNNAR could capture both the time and spatial relationship between activities while transferring knowledge. Experiments on three public activity recognition datasets demonstrate that: 1) The USSAR algorithm is effective in selecting the best source domains. 2) The TNNAR method can reach high accuracy when performing activity knowledge transfer.
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PDF链接：
https://arxiv.org/pdf/1807.07963
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