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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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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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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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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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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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Age-Unbiased Facial Emotion Recognition with Regularizing Self-Attention Value Vector 25th

Age-Unbiased Facial Emotion Recognition with Regularizing Se...

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25th International Conference on Intelligent Data Engineering and Automated Learning

作者： Park, Jaeil Cho, Sung-Bae Yonsei Univ Dept Comp Sci Seoul 03722 South Korea

ISBN: (纸本)9783031777301;9783031777318

Facial emotion recognition (FER) is a challenging task that involves analyzing emotions based on attributes entangled with the face in an image. The FER model inherently possesses age bias because age-related features such as wrinkles and skin texture can create confusion in understanding the emotions of the elderly and decrease accuracy. This paper proposes a FER method that mitigates age bias by applying adversarial regularization to the value vector of self-attention, which is related to age attributes, to minimize information loss. The proposed method focuses on removing biases without affecting the landmarks that make up facial emotions, as key and query vectors in the attention are associated with facial landmarks while value vector is associated with textural features of the image. An age discriminator interrupts age information in value vectors and restores information through the emotion classifier to minimize excessive loss. Experimental results with two FER datasets, RAF-DB and FACES, show that the proposed method yields superior accuracy while reducing bias compared to the state-of-the-art methods.

关键词： Facial emotion recognition bias mitigation self-attention regularization adversarial autoencoder

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adversarial autoencoders for denoising digitized historical documents: the use case of incunabula 3

Adversarial Autoencoders for denoising digitized historical ...

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15th IAPR International Conference on Document Analysis and Recognition (ICDAR) / 3rd International Workshop on Arabic and derived Script Analysis and Recognition (ASAR)

作者： Neji, Hala Nogueras-Iso, Javier Lacasta, Javier Ben Halima, Mohamed Alimi, Adel M. Univ Sfax Natl Engn Sch Sfax ENIS REGIM Lab Res Grp Intelligent Machines BP 1173 Sfax 3038 Tunisia Univ Gabes Gabes Tunisia Univ Zaragoza I3A Zaragoza Spain

ISBN: (纸本)9781728150543

Historical document denoising is the most challenging step in the field of image processing and computer vision. In this paper, we propose a novel end-to-end adversarial autoencoder (AAE) to generate clean images and to show how adversarial autoencoders can be used in historical document denoising. We used the adversarial autoencoder (AAE), which uses the generative adversarial networks (GAN) so as to suit the aggregated posterior of the hidden code vector of the autoencoder with an arbitrary prior. The experiments results prove that our approach functions more positively than the cutting-edge approaches on synthetic and real world images at a lower computational cost as well.

关键词： adversarial autoencoder GAN incunabula documents image denoising historical document images

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Denoising adversarial autoencoders: classifying skin lesions using limited labelled training data

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IET COMPUTER VISION 2018年第8期12卷 1105-1111页

作者： Creswell, Antonia Pouplin, Alison Bharath, Anil A. Imperial Coll BICV Bioengn London England DAMAE Med Paris France Data Sci Inst London England

The authors propose a novel deep learning model for classifying medical images in the setting where there is a large amount of unlabelled medical data available, but the amount of labelled data is limited. They consider the specific case of classifying skin lesions as either benign or malignant. In this setting, the authors' proposed approach - the semi-supervised, denoising adversarial autoencoder - is able to utilise vast amounts of unlabelled data to learn a representation for skin lesions, and small amounts of labelled data to assign class labels based on the learned representation. They perform an ablation study to analyse the contributions of both the adversarial and denoising components and compare their work with state-of-the-art results. They find that their model yields superior classification performance, especially when evaluating their model at high sensitivity values.

关键词： pattern classification feature extraction skin medical image processing image classification learning (artificial intelligence) adversarial autoencoder skin lesions labelled training data deep learning model classifying medical images unlabelled medical data labelled data specific case vast amounts unlabelled data class labels learned representation denoising components model yields superior classification performance

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Deep Mixture of adversarial autoencoders Clustering Network 4th

Deep Mixture of Adversarial Autoencoders Clustering Network

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4th Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

作者： Liu, Aofu Ji, Zexuan Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China

ISBN: (纸本)9783030880040;9783030880033

As one of the most fundamental technologies in machine learning, unsupervised clustering methods have been vastly promoted due to the rapid development of deep learning. A widely utilized assumption is that the data is a set of low-dimensional manifolds, thus the key challenge for clustering methods is to ensure the potential features be completely separated. In this paper, we propose a mixture of adversarial autoencoders clustering (MAAE) network to solve the above problem. The data of each cluster is represented by one adversarial autoencoder. By introducing the adversarial information, the aggregated posterior of the hidden code vector of the autoencoder can better match with the prior distribution which is constructed based on the generalized Gaussian mixture model. So the latent features embedded in data can be well separated. The clustering network jointly learns the nonlinear data representation and the collection of adversarial autoencoders. The clustering results are obtained byminimizing the reconstruction loss of the mixture of adversarial autoencoders network. We perform experiments on popular standard datasets, and the results show that the proposed algorithm is effective in both clustering and feature separation.

关键词： Clustering Mixture autoencoders adversarial autoencoder

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