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LLP-AAE: Learning from label proportions with adversarial autoencoder

NEUROCOMPUTING 2023年第1期537卷 282-295页

作者： Wang, Bo Sun, Yingte Tong, Qiang Univ Int Business & Econ Sch Informat Technol & Management Beijing 100029 Peoples R China

This paper presents an effective weakly supervised learning algorithm LLP-AAE to leverage the adversar-ial autoencoder (AAE) for learning from label proportions (LLP), in which only the bag-level proportional information is available. Our LLP-AAE utilizes an autoencoder backbone and performs adversarial training in latent space to match the aggregated posterior distribution of hidden coding with the prior distribu-tions. In this way, apart from the reconstruction task, the encoder is also dedicated to producing fake samples, in order to deceive discriminators as far as possible. Ultimately, the encoder is employed as a competent label predictor for unseen data. In addition to the LLP classifier, our model can also achieve controllable samples generation by feeding the decoder with gradually changing latent code, which is proven to be useful for a better LLP performance. We also provide a panoramic explanation for LLP-AAE by regarding the LLP problem as an alternative learning procedure between proportion-based pseudo label generation and discriminative reconstruction. Experiments on six benchmark image data -sets demonstrate the advantage of our method both in style manipulation with the latent feature repre-sentation and comparable multi-class LLP performance with the state-of-the-art models.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Learning from label proportions adversarial autoencoder Multi-class classification Weakly supervised learning

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AAE-Dpeak-SC: A novel unsupervised clustering method for space target ISAR images based on adversarial autoencoder and density peak-spectral clustering

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ADVANCES IN SPACE RESEARCH 2022年第5期70卷 1472-1495页

作者： Yang, Hong Ding, Wenzhe Yin, Canbin Beijing Inst Tracking & Telecommun Technol Beijing 100096 Peoples R China Space Engn Univ Beijing 101416 Peoples R China

Aiming at the problem of unlabeled ISAR image clustering of space targets, the paper proposes a new unsupervised clustering method based on adversarial autoencoder (AAE) and density peak-spectral clustering (Dpeak-SC). This method introduces the AAE into the unsupervised clustering problem of radar images for the first time and realizes the automatic extraction of the features of unlabeled space target ISAR image data. And on this basis, by combining the density peak algorithm with the spectral clustering algorithm, the ISAR image data is classified into multiple clusters with good clustering accuracy, clustering coincidence, and clustering similarity without providing any prior information. This method first implements the self-supervised training of the AAE model through the adversarial and reconstruction process. Then, it uses the trained encoder to extract the latent features of the ISAR image of the space target in the lowdimensional space. Next, a decision graph is constructed by calculating the local density and relative distance of the proposed features to determine the number of clusters in the hidden feature vector set. Finally, the spectral clustering algorithm is used to transform the problem into the graph's optimal partition problem. The clustering is realized by constructing and segmenting the undirected weight graph of the hidden feature vector set. The simulation test on the unlabeled space target ISAR image data verifies the method's effectiveness in this paper. (c) 2022 COSPAR. Published by Elsevier B.V. All rights reserved.

关键词： Space target unlabeled ISAR image Unsupervised clustering adversarial autoencoder Density peak clustering Spectral clustering

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An active learning method using deep adversarial autoencoder-based sufficient dimension reduction neural network for high-dimensional reliability analysis

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2024年 247卷

作者： Bao, Yuequan Sun, Huabin Guan, Xiaoshu Tian, Yuxuan Harbin Inst Technol Key Lab Smart Prevent Mitigat Civil Engn Disaster Minist Ind & Informat Technol Harbin 150090 Peoples R China Harbin Inst Technol Key Lab Struct Dynam Behav & Control Minist Educ Harbin 150090 Peoples R China Harbin Inst Technol Sch Civil Engn Harbin 150090 Peoples R China

Reliability analysis often requires time-consuming evaluations, especially when dealing with high-dimensional and nonlinear problems. To address this challenge, surrogate model methods are frequently employed. One way to improve the efficiency of surrogate model methods involves selecting informative samples that significantly enhance the accuracy of the surrogate model. This paper introduces a novel approach to facilitate the construction of surrogate models and selection of informative samples in high-dimensional reliability analysis, through an active learning method based on a deep adversarial autoencoder-based sufficient dimension reduction (AAE-SDR) neural network. The AAE-SDR neural network serves as a surrogate model, transforming complex high-dimensional variables into tractable, low-dimensional embeddings relevant to the target. These embeddings are Gaussian-distributed with a distinct latent limit state boundary. A new sampling strategy is proposed to select informative misclassified samples by iteratively identifying candidate samples near the latent limit state boundary and uniformly sampling from the candidate sample dataset based on the latent Gaussian distribution. The effectiveness of the proposed approach is demonstrated through two highdimensional numerical examples and a cable-stayed bridge case study. Results show that the proposed method simplifies complex high-dimensional reliability problems and provides a relatively accurate estimated failure probability with a limited number of samples.

关键词： Structural reliability Deep neural network Sufficient dimension reduction adversarial autoencoder Surrogate model

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Estimation of 6D Pose of Objects Based on a Variant adversarial autoencoder

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NEURAL PROCESSING LETTERS 2023年第7期55卷 9581-9596页

作者： Huang, Dan Ahn, Hyemin Li, Shile Hu, Yueming Lee, Dongheui South China Univ Technol Sch Mech & Automot Engn Guangzhou 510640 Peoples R China Tech Univ Munich Dept Elect & Comp Engn D-80333 Munich Germany South China Univ Technol Sch Automot Sci & Engn Guangzhou 510640 Peoples R China

The goal of this paper is to estimate object's 6D pose based on the texture-less dataset. The pose of each projection view is obtained by rendering the 3D model of each object, and then the orientation feature of the object is implicitly represented by the latent space obtained from the RGB image. The 3D rotation of the object is estimated by establishing the codebook based on a template matching architecture. To build the latent space from the RGB images, this paper proposes a network based on a variant adversarial autoencoder (Makhzani et al. in Computer Science, 2015). To train the network, we use the dataset without pose annotation, and the encoder and decoder do not have a structural symmetry. The encoder is inspired by the existing model (Yang et al. in proceedings of IJCAI, 2018), (Yang et al. in proceedings 11 of CVPR, 2019) that incorporates the function of feature extraction from two different streams. Based on this network, the latent feature vector that implicitly represents the orientation of the object is obtained from the RGB image. Experimental results show that the method in this paper can realize the 6D pose estimation of the object and the result accuracy is better than the advanced method (Sundermeyer et al. in proceedings of ECCV, 2018).

关键词： adversarial autoencoder 6D pose RGB image Self-supervised learning

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AUTH: An adversarial autoencoder Based Unsupervised Insider Threat Detection Scheme for Multisource Logs

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第9期20卷 10954-10965页

作者： Zhu, Xingjian Dong, Jiankuo Qi, Jin Zhou, Zhenguo Dong, Zhenjiang Sun, Yanfei Wang, Moyu Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210003 Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Nanjing 210003 Peoples R China Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing 210003 Peoples R China Nanjing Univ Posts & Telecommun Sch Internet Things Nanjing 210003 Peoples R China Nanjing Univ Posts & Telecommun Dept Internet Things Nanjing 210003 Peoples R China Nanjing Univ Posts & Telecommun Jiangsu Engn Res Ctr HPC & Intelligent Proc Nanjing 210003 Peoples R China Hefei Yun Micro Elect Co Ltd Hefei 230088 Peoples R China

Deep learning has shown broad research prospects in addressing insider threats, a serious problem currently facing industrial information systems. Although deep learning is able to capture effective feature representations from complex multidimensional data, there are still issues such as strong stealth of insider threat behavior and the imbalance data that need to be solved. Therefore, we propose an adversarial autoencoder based Unsupervised insider Threat detection scHeme (AUTH). Compared to other methods, AUTH fully considers the role of time feature and event feature in threat detection. In addition, in order to improve the performance of autoencoder models to detect covert threat behaviors, AUTH drives a temporal convolutional network and long short-term memory network-based adversarial autoencoder (TL-AAE). Generative adversarial Theory is introduced to solve the problem of uncertainty in the latent feature of the encoder. Finally, with the sufficient experiments on public datasets, we demonstrate that the usefulness of adding time features and the proposed TL-AAE model to improve threat detection performance. Compared with the baseline, AUTH obtains the area under curve value of 0.932, which is 4.95% higher than the highest result obtained by the baseline. In addition, AUTH obtains the EER value of 0.146, which is 12.57% lower than the lowest result of the baseline.

关键词： Feature extraction Threat assessment Machine learning Long short term memory Hidden Markov models Training Deep learning adversarial autoencoder insider threat detection time series data unsupervised learning

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AAE-SC: A scRNA-Seq Clustering Framework Based on adversarial autoencoder

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IEEE ACCESS 2020年 8卷 178962-178975页

作者： Wu, Yulun Guo, Yanming Xiao, Yandong Lao, Songyang Natl Univ Def Technol Coll Syst Engn Changsha 410072 Peoples R China

Single-cell RNA sequencing (scRNA-seq) provides the expression profiles of individual cells, and it is expected to provide higher cellular differential resolution than traditional bulk RNA sequencing. In scRNA-seq analysis, clustering is crucial for identifying cell types, and can be potentially exploited to understand high-level biological processes. Recently, autoencoder has been successfully applied in scRNA-seq clustering problem and achieved promising results. Most existing works focus on characterizing the sparsity of data, and directly utilize the bottleneck feature of the autoencoder for clustering might not be optimal. In this paper, a novel framework named adversarial autoencoder ScRNA-seq Clustering (AAE-SC) is proposed to bring an additional constraint on the bottleneck feature. Specifically, AAE-SC adds an AAE module on top of the bottleneck layer, and constrains the bottleneck feature distribution to be aligned with a consistent distribution. Also, the AAE and the reconstructed modules are jointly optimized to generate a highly discriminative and consistent feature, which is further proceeded for clustering. We find that by using AAE-SC to impose certain constraints on the features of the hidden layer, the performance of clustering can be improved. Experimental results on three real-world datasets demonstrate that the proposed AAE-SC framework outperformed state-of-the-art methods by 2% at least and 5% at most. And AAE-SC shows more robustness than the baseline model for downsampled and unbalanced cluster size datasets.

关键词： Clustering algorithms Machine learning Cells (biology) Measurement Clustering methods RNA Sequential analysis Single-cell RNA-seq data adversarial autoencoder clustering analysis unsupervised learning

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Research on Digital Camouflage Pattern Generation Algorithm Based on adversarial autoencoder Network

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2020年第6期34卷

作者： Yang, Xin Xu, Wei-Dong Jia, Qi Li, Ling Army Engn Univ Natl Key Lab Lightning Protect & Electromagnet Ca Nanjing 210007 Jiangsu Peoples R China

In the past, most of the digital camouflage used textural features to extract the configuration features of spots in gray images, unable to effectively utilize the position relationship between color information. In order to overcome this shortcoming, a new digital camouflage pattern design model was proposed based on the model of adversarial autoencoder network. Firstly, the complexity and performance of several main color extraction algorithms were analyzed and compared, and combined with AFK-MC2 algorithm and color similarity coefficient, a fast camouflage main color clustering method was proposed. Then a deep convolution adversarial autoencoder network was designed to extract and describe the configuration features of the spots in background pattern. In order to diffuse pixel spot and achieve the effect of spatial color blending, a morphological processing algorithm was proposed to process the generated camouflage patterns. Finally, two sets of grassland and woodland datasets were established, respectively. The influence of the number of latent variables of network on the training process was tested on the dataset, and the number of camouflage feature descriptions was determined to be greater than or equal to 10. In order to verify the effectiveness of the generated camouflage, the spots in background region and target region were randomly selected, and the Euclidean distance between the feature parameters of these spots was calculated. Both the visual and experimental results demonstrate that the generated spots have high fusion with the background.

关键词： Camouflage design adversarial autoencoder main color extraction spatial color blending

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Video anomaly detection and localization via multivariate gaussian fully convolution adversarial autoencoder

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NEUROCOMPUTING 2019年 369卷 92-105页

作者： Li, Nanjun Chang, Faliang Shandong Univ Sch Control Sci & Engn 17923 Jingshi Rd Jinan 250061 Shandong Peoples R China

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 space representations of normal samples are trained to accord with a specific prior distribution by the proposed deep neural network - Multivariate Gaussian Fully Convolution adversarial autoencoder (MGFC-AAE), while the latent representations of anomalies do not. In order to extract deep features from input samples as latent representations, a convolutional neural network (CNN) is employed for the encoder of the deep network. Based on the probability that the test sample is associated with the prior distribution, an energy-based method is applied to obtain its anomaly score. A two-stream framework is utilized to integrate the appearance and motion cues to achieve more comprehensive detection results, taking the gradient and optical flow patches as inputs for each stream. Besides, a multi-scale patch structure is put forward to handle the perspective of some video scenes. Experiments are conducted on three public datasets, results verify that our framework can accurately detect and locate abnormal objects in various video scenes, achieving competitive performance when compared with other state-of-the-art works. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Anomaly detection Video surveillance adversarial autoencoder 3D Gradient Optical flow Two-stream framework

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Semisupervised hyperspectral imagery classification based on a three-dimensional convolutional adversarial autoencoder model with low sample requirements

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JOURNAL OF APPLIED REMOTE SENSING 2020年第2期14卷

作者： Cao, Zeyu Li, Xiaorun Zhao, Liaoying Zhejiang Univ Coll Elect Engn Hangzhou Peoples R China HangZhou Dianzi Univ China Inst Comp Applicat Technol Hangzhou Peoples R China

Although there are many state-of-the-art methods for hyperspectral classification, data deficiency is a problem that should be addressed before popularizing hyperspectral technology. To solve this problem, it is worth exploring methods based on small datasets. Inspired by the advanced deep learning classification methods and the autoencoder structure, we propose a structure named three-dimensional convolutional adversarial autoencoder that combines the two processes for semisupervised hyperspectral classification. Our experiments show its utility in data-deficient situations, and our study analyzes its advantages and disadvantages, and points out a probable direction toward optimization. (C) 2020 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： hyperspectral image classification adversarial autoencoder semisupervised learning few samples

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Deep feature representation with online convolutional adversarial autoencoder for nonlinear process monitoring

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JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS 2024年 155卷

作者： Yang, Xu Xiao, Jieshi Huang, Jian Peng, Kaixiang Univ Sci & Technol Beijing Sch Automat & Elect Engn Key Lab Knowledge Automat Ind Proc Minist Educ Beijing 100083 Peoples R China

Background: The significant nonlinearity between the monitoring variables introduces challenges in the task of features extraction when implementing fault detection for an industrial process. Recently, neural network with complex hierarchical structure and layer-by-layer nonlinear transformation, especially autoencoder (AE), have attracted considerable attention from the process monitoring community. However, the latent features of AE cannot fully reflect process information, and there is redundancy between features. Methods: This study introduces an online convolutional adversarial autoencoder (AAE) model to learn nonlinear features with representative information of industrial processes. The structure of generative adversarial networks (GAN) in AAE aims to extract features that can reflect the manifold information and subject to the Gaussian distribution. Given the advantages of convolutional kernels in weight sharing and local perception, convolutional kernels are embedded in AAE to capture the spatial structure information of process data. On the basis of this model, the fault-relevant features selection strategy is designed to remove redundant information online and improve the accuracy of fault detection. Significant findings: The results show that the average fault detection rate of the penicillin fermentation process can be improved to 94% using the proposed algorithm comparing with the current fault detection methods.

关键词： Nonlinear process monitoring adversarial autoencoder Convolutional kernel Penicillin fermentation process

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