Search Results - "Adversarial Autoencoder"

Causal Adversarial Autoencoder for Disentangled SAR Image Representation and Few-Shot Target Recognition by Guo, Qian, Xu, Huilin, Xu, Feng

“…Lack of interpretability and weak generalization ability have become the major challenges with data-driven intelligent SAR-ATR technology, especially in…”
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Unsupervised learning-based framework for indirect structural health monitoring using adversarial autoencoder by Calderon Hurtado, A., Kaur, K., Makki Alamdari, M., Atroshchenko, E., Chang, K.C., Kim, C.W.

“…This paper studies the problem of bridge health monitoring in an unsupervised manner utilizing only the measured responses from a vehicle passing over a…”
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druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico by Kadurin, Artur, Nikolenko, Sergey, Khrabrov, Kuzma, Aliper, Alex, Zhavoronkov, Alex

“…Deep generative adversarial networks (GANs) are the emerging technology in drug discovery and biomarker development. In our recent work, we demonstrated a…”
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OTB-AAE: Semi-supervised anomaly detection on industrial images based on Adversarial Autoencoder with Output-Turn-Back structure by Li, Xuewei, Jing, Junfeng, Bao, Junmin, Lu, Pengwen, Xie, Yaohua, An, Ying

“…Due to the unbalanced proportion of positive (non-anomalous) and negative (anomalous) samples obtained from industrial data collection, the development…”
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Video anomaly detection and localization via multivariate gaussian fully convolution adversarial autoencoder by Li, Nanjun, Chang, Faliang

“…In this paper, we present a novel deep learning based method for video anomaly detection and localization. The key idea of our approach is that the latent…”
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Distilling from professors: Enhancing the knowledge distillation of teachers by Bang, Duhyeon, Lee, Jongwuk, Shim, Hyunjung

“…•We raise the issue that existing KD methods overlook the quality of soft targets in KD performance.•We propose the professor model to provide high-quality and…”
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Adversarial Autoencoder Network for Hyperspectral Unmixing by Jin, Qiwen, Ma, Yong, Fan, Fan, Huang, Jun, Mei, Xiaoguang, Ma, Jiayi

“…Spectral unmixing (SU), which refers to extracting basic features (i.e., endmembers) at the subpixel level and calculating the corresponding proportion (i.e.,…”
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Enhancing Structural Condition Assessment in Steel Pipelines via a WGAN–AAE Data Fusion Methodology by Hassani, Sahar, Mustapha, Samir, Mousavi, Mohsen, Li, Jianchun, Song, Chongmin, Dackermann, Ulrike

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An unsupervised adversarial autoencoder for cyber attack detection in power distribution grids by Zideh, Mehdi Jabbari, Khalghani, Mohammad Reza, Solanki, Sarika Khushalani

“…Detection of cyber attacks in smart power distribution grids with unbalanced configurations poses challenges due to the inherent nonlinear nature of these…”
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An active learning method using deep adversarial autoencoder-based sufficient dimension reduction neural network for high-dimensional reliability analysis by Bao, Yuequan, Sun, Huabin, Guan, Xiaoshu, Tian, Yuxuan

“…Reliability analysis often requires time-consuming evaluations, especially when dealing with high-dimensional and nonlinear problems. To address this…”
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The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology by Kadurin, Artur, Aliper, Alexander, Kazennov, Andrey, Mamoshina, Polina, Vanhaelen, Quentin, Khrabrov, Kuzma, Zhavoronkov, Alex

“…Recent advances in deep learning and specifically in generative adversarial networks have demonstrated surprising results in generating new images and videos…”
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A data-driven methodology for bridge indirect health monitoring using unsupervised computer vision by Hurtado, A. Calderon, Alamdari, M. Makki, Atroshchenko, E., Chang, K.C., Kim, C.W.

“…In recent years, researchers have extensively explored the application of drive-by inspection technology for bridge damage assessment. This approach involves…”
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Incorporating Geological Knowledge into Deep Learning to Enhance Geochemical Anomaly Identification Related to Mineralization and Interpretability by Zhang, Chunjie, Zuo, Renguang

“…Effective geochemical anomaly identification is crucial in mineral exploration. Recent trends have favored deep learning (DL) to decipher geochemical survey…”
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Adversarial Autoencoder Based Feature Learning for Fault Detection in Industrial Processes by Jang, Kyojin, Hong, Seokyoung, Kim, Minsu, Na, Jonggeol, Moon, Il

“…Deep learning has recently emerged as a promising method for nonlinear process monitoring. However, ensuring that the features from process variables have…”
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DeGAN - Decomposition-based unified anomaly detection in static networks by Tüzen, Ahmet, Yaslan, Yusuf

“…Graph anomaly detection aims to identify anomalous occurrences in networks. However, this is more challenging than the traditional anomaly detection problem…”
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Learning to Generate SAR Images With Adversarial Autoencoder by Song, Qian, Xu, Feng, Zhu, Xiao Xiang, Jin, Ya-Qiu

“…Deep learning-based synthetic aperture radar (SAR) target recognition often suffers from sparsely distributed training samples and rapid angular variations due…”
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Detecting in-vehicle intrusion via semi-supervised learning-based convolutional adversarial autoencoders by Hoang, Thien-Nu, Kim, Daehee

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A3N: Attention-based adversarial autoencoder network for detecting anomalies in video sequence by Aslam, Nazia, Rai, Prateek Kumar, Kolekar, Maheshkumar H.

“…This paper presents a novel attention-based adversarial autoencoder network (A3N) that consists of a two-stream decoder to detect abnormal events in video…”
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Evolutionary Adversarial Autoencoder for Unsupervised Anomaly Detection of Industrial Internet of Things by Zeng, Guo-Qiang, Yang, Yao-Wei, Lu, Kang-Di, Geng, Guang-Gang, Weng, Jian

“…The rapid growth of interconnected smart devices and advanced computing technologies in the industrial Internet of Things (IIoT) has significantly enhanced…”
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A new class of fault detection and diagnosis methods by fusion of spatially distributed and time-dependent features by Chen, Yan, Zhang, Xiaoyu, Li, Dazi, Zhou, Jinglin

“…Nonlinear, non-Gaussian, and dynamic features pose a great challenge for complex fault detection and fault diagnosis (FDD). Focusing on fault detection,…”
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