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A deep adversarial variational autoencoder model for dimensionality reduction in single-cell RNA sequencing analysis

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BMC BIOINFORMATICS 2020年第1期21卷 64-64页

作者： Lin, Eugene Mukherjee, Sudipto Kannan, Sreeram Univ Washington Dept Elect & Comp Engn Seattle WA 98195 USA Univ Washington Dept Biostat Seattle WA 98195 USA China Med Univ Grad Inst Biomed Sci Taichung Taiwan

Background Single-cell RNA sequencing (scRNA-seq) is an emerging technology that can assess the function of an individual cell and cell-to-cell variability at the single cell level in an unbiased manner. Dimensionality reduction is an essential first step in downstream analysis of the scRNA-seq data. However, the scRNA-seq data are challenging for traditional methods due to their high dimensional measurements as well as an abundance of dropout events (that is, zero expression measurements). Results To overcome these difficulties, we propose DR-A (Dimensionality Reduction with adversarial variational autoencoder), a data-driven approach to fulfill the task of dimensionality reduction. DR-A leverages a novel adversarial variational autoencoder-based framework, a variant of generative adversarial networks. DR-A is well-suited for unsupervised learning tasks for the scRNA-seq data, where labels for cell types are costly and often impossible to acquire. Compared with existing methods, DR-A is able to provide a more accurate low dimensional representation of the scRNA-seq data. We illustrate this by utilizing DR-A for clustering of scRNA-seq data. Conclusions Our results indicate that DR-A significantly enhances clustering performance over state-of-the-art methods.

关键词： adversarial autoencoder Variational autoencoder Dimensionality reduction Generative adversarial networks Single-cell RNA sequencing

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druGAN: An Advanced Generative adversarial autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico

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MOLECULAR PHARMACEUTICS 2017年第9期14卷 3098-3104页

作者： Kadurin, Artur Nikolenko, Sergey Khrabrov, Kuzma Aliper, Alex Zhavoronkov, Alex Johns Hopkins Univ Eastern Emerging Technol Ctr Insilico Med Inc Pharmaceut Artificial Intelligence Dept Baltimore MD 21218 USA Natl Res Univ Higher Sch Econ St Petersburg 190008 Russia Steklov Math Inst St Petersburg St Petersburg 191023 Russia Mail Ru Grp Ltd Search Dept Moscow 125167 Russia Biogerontol Res Fdn Trevissome Pk Truro TR4 8UN England Moscow Inst Phys & Technol Dolgoprudnyi 141701 Russia Kazan Fed Univ Kazan 420008 Republic Of Tat Russia

Deep generative adversarial networks (GANs) are the emerging technology in drug discovery and biomarker development. In our recent work, we demonstrated a proof-of-concept of implementing deep generative adversarial autoencoder (AAE) to identify new molecular fingerprints with predefined anticancer properties. Another popular generative model is the variational autoencoder (VAE), which is based on deep neural architectures. In this work, we developed an advanced AAE model for molecular feature extraction problems, and demonstrated its advantages compared to VAE in terms of (a) adjustability in generating molecular fingerprints;(b) capacity of processing very large molecular data sets;and (c) efficiency in unsupervised pretraining for regression model. Our results suggest that the proposed AAE model significantly enhances the capacity and efficiency of development of the new molecules with specific anticancer properties using the deep generative models.

关键词： adversarial autoencoder deep learning drug discovery variational autoencoder generative adversarial network

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Semantic-guided autoencoder adversarial hashing for large-scale cross-modal retrieval

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COMPLEX & INTELLIGENT SYSTEMS 2022年第2期8卷 1603-1617页

作者： Li, Mingyong Li, Qiqi Ma, Yan Yang, Degang Chongqing Normal Univ Coll Comp & Informat Sci Chongqing 401331 Peoples R China

With the vigorous development of mobile Internet technology and the popularization of smart devices, while the amount of multimedia data has exploded, its forms have become more and more diversified. People's demand for information is no longer satisfied with single-modal data retrieval, and cross-modal retrieval has become a research hotspot in recent years. Due to the strong feature learning ability of deep learning, cross-modal deep hashing has been extensively studied. However, the similarity of different modalities is difficult to measure directly because of the different distribution and representation of cross-modal. Therefore, it is urgent to eliminate the modal gap and improve retrieval accuracy. Some previous research work has introduced GANs in cross-modal hashing to reduce semantic differences between different modalities. However, most of the existing GAN-based cross-modal hashing methods have some issues such as network training is unstable and gradient disappears, which affect the elimination of modal differences. To solve this issue, this paper proposed a novel Semantic-guided autoencoder adversarial Hashing method for cross-modal retrieval (SAAH). First of all, two kinds of adversarial autoencoder networks, under the guidance of semantic multi-labels, maximize the semantic relevance of instances and maintain the immutability of cross-modal. Secondly, under the supervision of semantics, the adversarial module guides the feature learning process and maintains the modality relations. In addition, to maintain the inter-modal correlation of all similar pairs, this paper use two types of loss functions to maintain the similarity. To verify the effectiveness of our proposed method, sufficient experiments were conducted on three widely used cross-modal datasets (MIRFLICKR, NUS-WIDE and MS COCO), and compared with several representatives advanced cross-modal retrieval methods, SAAH achieved leading retrieval performance.

关键词： Deep hashing Cross-modal retrieval adversarial autoencoder adversarial learning

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ADAE: adversarial DISTRIBUTED SOURCE autoencoder FOR POINT CLOUD COMPRESSION

ADAE: ADVERSARIAL DISTRIBUTED SOURCE AUTOENCODER FOR POINT C...

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IEEE International Conference on Image Processing (ICIP)

作者： Milani, Simone Univ Padova Dept Informat Engn Padua Italy

ISBN: (纸本)9781665441155

The current paper presents an adversarial autoencoding strategy for voxelized point cloud geometry based on the principles of distributed source coding. The encoder characterizes the input voxel blocks with an array of hash bytes while the decoder combines them with side information blocks in order to reconstruct the original data. The reconstruction process is optimized by classifying the reconstructed block with an adversarial discriminator in order to make the recovered data as close as possible to an original block. Experimental results show that the proposed solution generalizes well while obtaining better coding performance with respect to other state-of-the-art solutions and allowing high flexibility in rate shaping and decoding operations.

关键词： point cloud compression adversarial autoencoder distributed source coding decoding loss

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Multi-modal data novelty detection with adversarial autoencoders

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APPLIED SOFT COMPUTING 2024年 165卷

作者： Chen, Zeqiu Zhao, Kaiyi Sun, Ruizhi China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China Minist Agr Sci Res Base Integrated Technol Precis Agr Anim Hu Beijing 100083 Peoples R China

Novelty detection is usually defined as the identification of new or abnormal objects (outliers) from the normal ones (inliers), which has wide potential applications including instrument fault, credit card theft warning, and disease diagnosis in the real world. Most of the existing researches focus on the novelty detection for single- modal data, which may fail to provide an accurate and reliable decision because the single-modal data sometimes cannot fully reflect the actual condition of the problem. However, research on the novelty detection from multiple sources is limited. This study developed an end-to-end deep learning architecture of novelty detection for multi-modal data based on adversarial autoencoders (AAE), which requires no input data of the novelty class during the training process. The proposed model consists of three deep networks, which are trained to compete with each other, collaborate to understand the underlying concept in the normal class, and thus classify the testing samples. Among the three networks, two of them work as the novelty detectors, and the other one called the generator will support the two novelty detectors by enhancing the inliers and distorting the outliers. In addition, a pseudo novelty mechanism is developed to expand the groups of adversarial training with the aim to greatly improve the precision and robustness of the whole model. Then the proposed model is employed for the novelty detection based on the multi-modal data that is composed of images, audios, videos, and texts, etc. The experimental results on the PKU FG-XMedia dataset and the MSR-VTT dataset reveal that our proposed method learns the normal class effectively and is superior to the baseline and state-of-the-art methods.

关键词： Novelty detection Multi-modal data adversarial autoencoder Pseudo novelty sample

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Self-Attention-Based Multivariate Anomaly Detection for CPS Time Series Data with adversarial autoencoders 41

Self-Attention-Based Multivariate Anomaly Detection for CPS ...

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第41届中国控制会议

作者： Qiwen Li Tijin Yan Huanhuan Yuan Yuanqing Xia School of Mathematics and Statistics Beijing Institute of Technology School of Automation Beijing Institute of Technology School of Astronautics Northwestern Polytechnical University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Data-driven anomaly detection continues to be challenging due to the increased complexity of modern cyber physical systems(CPS s) and their temporal *** detection techniques are widely used through VAE-based frameworks and RNN-based deep learning ***,VAE and its variants impose too much constraint on extracted latent code,and RNN's autoregressive essence indicates the shortage of parallelism and long-term *** tackle the above issues,we propose TransAAE(Transformer-augmented adversarial autoencoder),a novel unsupervised approach for multivariate time series anomaly *** use of the adversarial autoencoder(AAE) architecture loosens the regularization of latent code,and self-attention mechanism is utilized to extract temporal *** experiments show an average F1 score over 0.9 on three public datasets,which significantly outperforms among the baselines.

关键词： adversarial autoencoder Self-Attention Mechanism Cyber Physical Systems Anomaly Detection

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DEEP SEMANTIC adversarial HASHING BASED ON autoencoder FOR LARGE-SCALE CROSS-MODAL RETRIEVAL

DEEP SEMANTIC ADVERSARIAL HASHING BASED ON AUTOENCODER FOR L...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Li, Mingyong Wang, Hongya Donghua Univ Coll Comp Sci & Technol Shanghai Peoples R China Chongqing Normal Univ Coll Comp & Informat Sci Chongqing Peoples R China

ISBN: (纸本)9781728114859

Thanks to the powerful feature learning capabilities of deep learning, some studies have introduced GANs into the cross-modal hashing. However, The GAN-based hashing methods are generally unstable and difficult to train in the process of adversarial learning. To address this problem, we propose a novel autoencoder Semantic adversarial Hashing for cross-modal retrieval (AESAH). Specifically, under the guidance of semantic multi-label, two types of adversarial autoencoder networks (inter-modality and intra-modality) are adopted to maximize the semantic relevance and maintain the invariance of cross-modal. Under semantic supervised, the adversarial modules guide the feature learning process, thus the modal relationship in both the common feature space and the common hamming space is maintained. Furthermore, in order to preserve the inter-modal correlation of all similar item pairs is higher than those of dissimilar ones, we use an inter-modal invariance triplet loss and a classification prediction loss to maintain *** experiments were carried out on two commonly used cross-modal datasets, compared with several existing cross-modal retrieval methods, AESAH has better retrieval performance.

关键词： Cross-modal retrieval deep hashing adversarial autoencoder

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A new class of fault detection and diagnosis methods by fusion of spatially distributed and time-dependent features

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JOURNAL OF PROCESS CONTROL 2025年 146卷

作者： Chen, Yan Zhang, Xiaoyu Li, Dazi Zhou, Jinglin Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China

Nonlinear, non-Gaussian, and dynamic features pose a great challenge for complex fault detection and fault diagnosis (FDD). Focusing on fault detection, independent component analysis (ICA) and adversarial autoencoder (AAE) are fused to form a new method for nonlinear non-Gaussian latent variable extraction: ICA-AAE. In addition, a strategy for establishing more accurate fault detection thresholds using tail distribution features is presented. Furthermore, a new class of fault diagnosis frameworks to fully exploit the information obtained from normal samples is developed. Fault data are first re-represented using the established ICA-AAE model. Then, the low-dimensional spatial distribution features with their inherited high-dimensional temporal dependencies are synthesized into image information using an image-based approach, and a spatio-temporal fusion fault diagnosis method is implemented using a convolutional neural network (CNN). Tennessee Eastman (TE) process results show that the proposed methods can achieve more accurate fault detection and diagnosis.

关键词： Fault diagnosis Process monitoring Independent component analysis Non-Gaussian Nonlinear process adversarial autoencoder

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Coordinated Distributionally Robust Optimal Allocation of Energy Storage System for HV-MV Distribution Network Resilience Enhancement

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2025年第2期61卷 2011-2024页

作者： Cao, Kuan Liu, Yutian Wang, Chunyi Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan 250061 Peoples R China State Grid Shandong Elect Power Co Dev Planning Dept Jinan 250001 Peoples R China

To solve the problem of power imbalance under extreme and normal scenarios in high voltage (HV) and middle voltage (MV) distribution networks with high penetrations of photovoltaic (PV), the paper proposes a distributionally robust optimal allocation method of energy storage system (ESS) for equilibrating resilience and economy. Firstly, due to the strong relation between resilience enhancement and ESS location, the siting-sizing sequential updating method is adopted to site ESS based on the planning resilience indexes. Secondly, to generate normal and extreme PV-load scenarios with continuous labels, an adversarial autoencoder (AAE) combined with transfer learning is improved by introducing the conditional neural network. Based on the improved AAE and clustering techniques, a Kullback-Leibler divergence-based ambiguity set is constructed to characterize the joint probability distribution of PV and load. Thirdly, considering the boundary information interaction of HV-MV distribution networks, a two-stage coordinated distributionally robust optimization model is established to size ESS. In the model, centralized ESS in HV distribution network participates in frequency regulation and peak shaving while distributed ESS in MV distribution network regulates voltage profiles in the four-quadrant mode. For further improving resilience, an operational resilience index as a chance constraint considering extreme scenarios is embedded into the model. Fourthly, the C&CG algorithm is nested into the analytical target cascading method to handle the model. Finally, case studies show that the proposed method can effectively balance resilience enhancement and economic operation.

关键词： adversarial autoencoder distributionally robust optimization ESS allocation ESS allocation HV-MV distribution network HV-MV distribution network resilience resilience resilience

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TFAM-AAE-Uk: A Dual-Metric Spectrum Anomaly Detection Algorithm

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IEEE COMMUNICATIONS LETTERS 2024年第11期28卷 2638-2642页

作者： Ji, Haipeng Zhang, Tao Qiao, Xiaoqiang Wu, Hao Gui, Guan Nanjing Univ Informat Sci & Technol Sch Elect & Informat Engn Nanjing 210044 Peoples R China Natl Univ Def Technol Res Inst 63 Nanjing 210007 Peoples R China Nanjing Univ Posts & Telecommun Coll Telecommun & Informat Engn Nanjing 210003 Peoples R China

Effective spectrum management critically depends on the ability to detect anomalies caused by both legal user (LU) violations and illegal user (IU) intrusions. In this study, we introduce TFAM-AAE-U-k, an innovative spectrum anomaly detection framework that integrates an adversarial autoencoder (AAE) with a Time Frequency Attention Mechanism (TFAM) and a specialized user discriminator, U-k. This framework first employs the TFAM-AAE to reconstruct spectrum data by exploiting latent, time, and frequency features of the input. The reconstruction error serves as the primary anomaly detection metric. To address the challenges posed by low interference-to- signal ratio (ISR) IUs and camouflaged LUs, U-k is utilized to extract fingerprint features vectors from IQ data, establishing class center vectors for each LU. The Mahalanobis distance between these vectors is then calculated as a secondary metric for anomaly detection. Our experimental results demonstrate that TFAM-AAE-U-k consistently achieves excellent detection performance, particularly in scenarios involving challenging and hard-to-detect anomalies.

关键词： Feature extraction Vectors Anomaly detection Time-frequency analysis Training Fingerprint recognition Law Spectrum anomaly detection adversarial autoencoder time frequency attention mechanism openset recognition

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