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摘要翻译：
本文介绍了一种基于深度学习的生成性感知框架，该框架将低端传感器与计算智能相结合，以获得与高端传感器相当的高识别精度。提出的生成式感知框架旨在将低端、低质量的传感器数据转化为更高质量的传感器数据，以实现分类精度。低端数据可以转换为相同模态的高质量数据或转换为另一模态的数据。与现有的图像生成方法不同的是，该框架不是基于相似性度量，而是基于判别模型和目标来最大限度地提高识别精度。这是通过在深度神经网络(DNN)中引入选择性特征再生来实现的。所提出的生成感知将从本质上把低质量的传感器数据转化为高质量的信息，以实现稳健的感知。给出的结果说明了该框架的性能。
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英文标题：
《Generative Sensing: Transforming Unreliable Sensor Data for Reliable
Recognition》
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作者：
Lina Karam and Tejas Borkar and Yu Cao and Junseok Chae
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最新提交年份：
2018
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Computer Vision and Pattern Recognition 计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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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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英文摘要：
This paper introduces a deep learning enabled generative sensing framework which integrates low-end sensors with computational intelligence to attain a high recognition accuracy on par with that attained with high-end sensors. The proposed generative sensing framework aims at transforming low-end, low-quality sensor data into higher quality sensor data in terms of achieved classification accuracy. The low-end data can be transformed into higher quality data of the same modality or into data of another modality. Different from existing methods for image generation, the proposed framework is based on discriminative models and targets to maximize the recognition accuracy rather than a similarity measure. This is achieved through the introduction of selective feature regeneration in a deep neural network (DNN). The proposed generative sensing will essentially transform low-quality sensor data into high-quality information for robust perception. Results are presented to illustrate the performance of the proposed framework.
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PDF链接：
https://arxiv.org/pdf/1801.02684
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