Coupled Deep Autoencoder for Single Image Super-Resolution

Zeng, Kun; Yu, Jun; Wang, Ruxin; Li, Cuihua; Tao, Dacheng

doi:10.1109/tcyb.2015.2501373

Cited by 202 publications

(84 citation statements)

References 46 publications

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“…We first provide relevant literatures in solving inverse problems from other domains. In particular, we focus on deep neural network related techniques [1,14,19,49,50,55]. In general, those different deep-learning based methods for solving inverse problems can be categorized into four types [29]: 1) to learn an end-to-end regression with vanilla convolutional neural network (CNN), 2) to learn higher-level representation, 3) to gradual refinement of inversion procedure, and 4) to incorporate with analytical methods and to learn a denoiser.…”

Section: A Data-driven Inverse Problemsmentioning

confidence: 99%

Data-Driven Seismic Waveform Inversion: A Study on the Robustness and Generalization

Zhang

Lin

2020

IEEE Trans. Geosci. Remote Sensing

117

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Acoustic-and elastic-waveform inversion is an important and widely used method to reconstruct subsurface velocity image. Waveform inversion is a typical non-linear and ill-posed inverse problem. Existing physics-driven computational methods for solving waveform inversion suffer from the cycle skipping and local minima issues, and not to mention solving waveform inversion is computationally expensive. In recent years, data-driven methods become a promising way to solve the waveform inversion problem. However, most deep learning frameworks suffer from generalization and over-fitting issue. In this paper, we developed a real-time data-driven technique and we call it VelocityGAN, to accurately reconstruct subsurface velocities. Our VelocityGAN is built on a generative adversarial network (GAN) and trained end-to-end to learn a mapping function from the raw seismic waveform data to the velocity image. Different from other encoder-decoder based data-driven seismic waveform inversion approaches, our VelocityGAN learns regularization from data and further impose the regularization to the generator so that inversion accuracy is improved. We further develop a transfer learning strategy based on VelocityGAN to alleviate the generalization issue. A series of experiments are conducted on the synthetic seismic reflection data to evaluate the effectiveness, efficiency, and generalization of VelocityGAN. We not only compare it with existing physics-driven approaches and data-driven frameworks but also conduct several transfer learning experiments. The experiment results show that VelocityGAN achieves state-of-the-art performance among the baselines and can improve the generalization results to some extent.

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Section: A Data-driven Inverse Problemsmentioning

confidence: 99%

Data-Driven Seismic Waveform Inversion: A Study on the Robustness and Generalization

Zhang

Lin

2020

IEEE Trans. Geosci. Remote Sensing

117

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“…It efficiently learns the patterns in data, encodes it, squeezes, or expand it, and reconstructs it to the original form by utilizing the ANN backpropagation technique to tune its parameters and reduce the error rate. Autoencoders have been investigated in research areas such as image super-resolution [24] and denoising [25,26]. Ribeiro et al [27] used the reconstruction error of appearance and motion features with a combination of video frames from a convolutional autoencoder to detect anomalous behavior.…”

Section: Deep Autoencoder For Feature Learningmentioning

confidence: 99%

Deep Learning Assisted Buildings Energy Consumption Profiling Using Smart Meter Data

Ullah

Haydarov

Haq

et al. 2020

Sensors

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The exponential growth in population and their overall reliance on the usage of electrical and electronic devices have increased the demand for energy production. It needs precise energy management systems that can forecast the usage of the consumers for future policymaking. Embedded smart sensors attached to electricity meters and home appliances enable power suppliers to effectively analyze the energy usage to generate and distribute electricity into residential areas based on their level of energy consumption. Therefore, this paper proposes a clustering-based analysis of energy consumption to categorize the consumers’ electricity usage into different levels. First, a deep autoencoder that transfers the low-dimensional energy consumption data to high-level representations was trained. Second, the high-level representations were fed into an adaptive self-organizing map (SOM) clustering algorithm. Afterward, the levels of electricity energy consumption were established by conducting the statistical analysis on the obtained clustered data. Finally, the results were visualized in graphs and calendar views, and the predicted levels of energy consumption were plotted over the city map, providing a compact overview to the providers for energy utilization analysis.

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“…In order to build a deep neural network, we apply SAEs model which consists of multiple layers of sparse AutoEncoders to extract features [40,41]. An AutoEncoder (AE) has three layers: input layer, hidden layer, and output layer.…”

Section: Sae-based Malware Detectionmentioning

confidence: 99%

Malware Detection Based on Deep Learning of Behavior Graphs

Xiao

Lin

Sun

et al. 2019

Mathematical Problems in Engineering

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The Internet of Things (IoT) provides various benefits, which makes smart device even closer. With more and more smart devices in IoT, security is not a one-device affair. Many attacks targeted at traditional computers in IoT environment may also aim at other IoT devices. In this paper, we consider an approach to protect IoT devices from being attacked by local computers. In response to this issue, we propose a novel behavior-based deep learning framework (BDLF) which is built in cloud platform for detecting malware in IoT environment. In the proposed BDLF, we first construct behavior graphs to provide efficient information of malware behaviors using extracted API calls. We then use a neural network-Stacked AutoEncoders (SAEs) for extracting high-level features from behavior graphs. The layers of SAEs are inserted one after another and the last layer is connected to some added classifiers. The architecture of the SAEs is 6,000-2,000-500. The experiment results demonstrate that the proposed BDLF can learn the semantics of higher-level malicious behaviors from behavior graphs and further increase the average detection precision by 1.5%.

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Coupled Deep Autoencoder for Single Image Super-Resolution

Cited by 202 publications

References 46 publications

Data-Driven Seismic Waveform Inversion: A Study on the Robustness and Generalization

Data-Driven Seismic Waveform Inversion: A Study on the Robustness and Generalization

Deep Learning Assisted Buildings Energy Consumption Profiling Using Smart Meter Data

Malware Detection Based on Deep Learning of Behavior Graphs

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