Multiview Generative Adversarial Network and Its Application in Pearl Classification

Xuan, Qi; Chen, Zhuangzhi; Liu, Yi; Huang, Huimin; Bao, Guanjun; Zhang, Dan

doi:10.1109/tie.2018.2885684

Cited by 103 publications

(53 citation statements)

References 29 publications

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“…However, unlike the architecture, the power variation of different parameter values is very insignificant, unless it is zero. it is observed that many AI models use at least (3,224 partial pre-trained parameters. Moreover, some advanced AI chips have hardware-level parameter pruning to improve the computational efficiency while maintaining the performance.…”

Section: B Parameter Sparsity Modelmentioning

confidence: 99%

Open DNN Box by Power Side-Channel Attack

Xiang

Chen

et al. 2020

IEEE Trans. Circuits Syst. II

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Deep neural networks are becoming popular and important assets of many AI companies. However, recent studies indicate that they are also vulnerable to adversarial attacks. Adversarial attacks can be either white-box or black-box. The white-box attacks assume full knowledge of the models while the black-box ones assume none. In general, revealing more internal information can enable much more powerful and efficient attacks. However, in most real-world applications, the internal information of embedded AI devices is unavailable, i.e., they are black-box. Therefore, in this work, we propose a side-channel information based technique to reveal the internal information of black-box models. Specifically, we have made the following contributions: (1) we are the first to use side-channel information to reveal internal network architecture in embedded devices;(2) we are the first to construct models for internal parameter estimation; and (3) we validate our methods on real-world devices and applications. The experimental results show that our method can achieve 96.50% accuracy on average. Such results suggest that we should pay strong attention to the security problem of many AI applications, and further propose corresponding defensive strategies in the future.Index Terms-Deep learning, machine learning, model identification, side-channel attack, adversarial attacks.

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Section: B Parameter Sparsity Modelmentioning

confidence: 99%

Open DNN Box by Power Side-Channel Attack

Xiang

Chen

et al. 2020

IEEE Trans. Circuits Syst. II

Self Cite

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“…Alternatively, the representation of data at a deeper level reveals inherent features and becomes more attractive. Recently, increasing applications of deep neural networks (DNNs) have been reported, especially in the speech recognition and computer vision fields [21][22][23][24][25][26][27][28][29]. As a popular DNN, the deep brief network (DBN) comprises multiple layers for representing data with multilevel abstraction [22].…”

Section: Introductionmentioning

confidence: 99%

Robust Soft Sensor with Deep Kernel Learning for Quality Prediction in Rubber Mixing Processes

Zheng

Liu

et al. 2020

Sensors

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Although several data-driven soft sensors are available, online reliable prediction of the Mooney viscosity in industrial rubber mixing processes is still a challenging task. A robust semi-supervised soft sensor, called ensemble deep correntropy kernel regression (EDCKR), is proposed. It integrates the ensemble strategy, deep brief network (DBN), and correntropy kernel regression (CKR) into a unified soft sensing framework. The multilevel DBN-based unsupervised learning stage extracts useful information from all secondary variables. Sequentially, a supervised CKR model is built to explore the relationship between the extracted features and the Mooney viscosity values. Without cumbersome preprocessing steps, the negative effects of outliers are reduced using the CKR-based robust nonlinear estimator. With the help of ensemble strategy, more reliable prediction results are further obtained. An industrial case validates the practicality and reliability of EDCKR.

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“…In such a situation, it is not suitable to directly apply SPC methods to flooding prognosis. Recent popular deep‐learning methods, such as deep brief networks , and convolutional neural networks , , often require a large amount of labeled data, which may not be directly applied to flooding prognosis. Recently, a degree of steadiness (DOS)‐based flooding prognosis strategy was proposed .…”

Section: Introductionmentioning

confidence: 99%

Dynamic Profile Monitoring for Flooding Prognosis in Packed Columns

et al. 2019

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In the chemical industry, real-time flooding prognosis is a necessity for packedcolumn operation because the flooding phenomenon interferes with the performance of production systems. In this work, the profile monitoring technique is utilized to capture the dynamic behavior of pressure drop, which is an important indicator for the flooding phenomenon. In each moving window, the pressure drop signals are described by using an exponential generalized autoregressive conditional heteroskedastic model. The onset of the flooding phenomenon is then indicated by changes in model parameters. Moreover, to efficiently capture the process change, a nonparametric approach is utilized to establish a statistical control chart. Experimental and comparison results show the advantages of the proposed method.

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Multiview Generative Adversarial Network and Its Application in Pearl Classification

Cited by 103 publications

References 29 publications

Open DNN Box by Power Side-Channel Attack

Open DNN Box by Power Side-Channel Attack

Robust Soft Sensor with Deep Kernel Learning for Quality Prediction in Rubber Mixing Processes

Dynamic Profile Monitoring for Flooding Prognosis in Packed Columns

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