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Zero-shot learning via visual-semantic aligned autoencoder

MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第8期20卷 14081-14095页

作者： Wei, Tianshu Huang, Jinjie Jin, Cong Harbin Univ Sci & Technol Sch Comp Sci & Technol Harbin 150006 Peoples R China Harbin Univ Sci & Technol Sch Automat Harbin 150006 Peoples R China

Zero-shot learning recognizes the unseen samples via the model learned from the seen class samples and semantic features. Due to the lack of information of unseen class samples in the training set, some researchers have proposed the method of generating unseen class samples by using generative models. However, the generated model is trained with the training set samples first, and then the unseen class samples are generated, which results in the features of the unseen class samples tending to be biased toward the seen class and may produce large deviations from the real unseen class samples. To tackle this problem, we use the autoencoder method to generate the unseen class samples and combine the semantic features of the unseen classes with the proposed new sample features to construct the loss function. The proposed method is validated on three datasets and showed good results.

关键词： generalized zero-shot learning conventional zero-shot learning autoencoder generated samples modalities alignment

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Feature Space of XRD Patterns Constructed by an autoencoder

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ADVANCED THEORY AND SIMULATIONS 2023年第2期6卷

作者： Utimula, Keishu Yano, Masao Kimoto, Hiroyuki Hongo, Kenta Nakano, Kousuke Maezono, Ryo JAIST Sch Mat Sci Asahidai 1-1 Nomi Ishikawa 9231292 Japan Toyota Motor Co Ltd 1 Toyota Cho Toyota Aichi 4718572 Japan JAIST Res Ctr Adv Comp Infrastruct Asahidai 1-1 Nomi Ishikawa 9231292 Japan JAIST Sch Informat Sci Asahidai 1-1 Nomi Ishikawa 9231292 Japan

X-ray diffraction (XRD) is commonly used to analyze systematic variations occurring in compounds to tune their material properties. Machine learning can be used to extract such significant systematics among a series of observed peak patterns. The feature space concept, in the context of autoencoders, can be the platform for performing such extractions, where each peak pattern is projected into a space to extract the systematics. Herein, an autoencoder is trained to learn to detect the systematics driven by atomic substitutions within a single phase without structural transitions. The feature space constructed by the trained autoencoder classifies the substitution compositions of XRD patterns satisfactorily. The compositions interpolated in the feature space are in good agreement with those of an XRD pattern projected to a point. Subsequently, the autoencoder generates a virtual XRD pattern from an interpolated point in the feature space. When the feature space is effectively optimized by enough training data, the autoencoder predicts an XRD pattern with a concentration, which is difficult to be described using the possible resolution of the supercell method of ab initio calculations.

关键词： autoencoder feature extraction machine learning materials informatics X-ray diffraction

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A Semi-Supervised autoencoder-Based Approach for Protein Function Prediction

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2022年第10期26卷 4957-4965页

作者： Dhanuka, Richa Tripathi, Anushree Singh, Jyoti P. Natl Inst Technol Patna Patna 800005 Bihar India

After the development of next-generation sequencing techniques, protein sequences are abundantly available. Determining the functional characteristics of these proteins is costly and time-consuming. The gap between the number of protein sequences and their corresponding functions is continuously increasing. Advanced machine-learning methods have stepped up to fill this gap. In this work, an advanced deep-learning-based approach is proposed for protein function prediction using protein sequences. A set of autoencoders is trained in a semi-supervised manner with protein sequences. Each autoencoder corresponds to a single protein function only. In particular, 932 autoencoders corresponding to 932 biological processes and 585 autoencoders corresponding to 585 molecular functions are trained separately. Reconstruction losses of each protein sample for every autoencoder are used as a feature to classify these sequences into their corresponding functions. The proposed model is tested on test protein samples and achieves promising results. This method can be easily extended to predict any number of functions having an ample amount of supporting protein sequences. All relevant codes, data and trained models are available at https://***/richadhanuka/PFP-autoencoders.

关键词： Proteins Protein engineering Predictive models Data models Protein sequence Feature extraction Biological system modeling autoencoder bioinformatics machine learning multi-label classification protein function

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LSTM-autoencoder Based Detection of Time-Series Noise Signals for Water Supply and Sewer Pipe Leakages

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WATER 2024年第18期16卷 2631页

作者： Shin, Yungyeong Na, Kwang Yoon Kim, Si Eun Kyung, Eun Ji Choi, Hyun Gyu Jeong, Jongpil Sungkyunkwan Univ Dept Smart Factory Convergence 2066 Seobu Ro Suwon 16419 South Korea SC Solut Global Co Ltd AI Res Lab 13 Heungdeok 1 roGyeonggi do Yongin 16954 South Korea

The efficient management of urban water distribution networks is crucial for public health and urban development. One of the major challenges is the quick and accurate detection of leaks, which can lead to water loss, infrastructure damage, and environmental hazards. Many existing leak detection methods are ineffective, especially in complex and aging pipeline networks. If these limitations are not overcome, it can result in a chain of infrastructure failures, exacerbating damage, increasing repair costs, and causing water shortages and public health risks. The leak issue is further complicated by increasing urban water demand, climate change, and population growth. Therefore, there is an urgent need for intelligent systems that can overcome the limitations of traditional methodologies and leverage sophisticated data analysis and machine learning technologies. In this study, we propose a reliable and advanced method for detecting leaks in water pipes using a framework based on Long Short-Term Memory (LSTM) networks combined with autoencoders. The framework is designed to manage the temporal dimension of time-series data and is enhanced with ensemble learning techniques, making it sensitive to subtle signals indicating leaks while robustly dealing with noise signals. Through the integration of signal processing and pattern recognition, the machine learning-based model addresses the leak detection problem, providing an intelligent system that enhances environmental protection and resource management. The proposed approach greatly enhances the accuracy and precision of leak detection, making essential contributions in the field and offering promising prospects for the future of sustainable water management strategies.

关键词： leak detection water supply LSTM autoencoder machine learning time series noise signals urban water management

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Modified autoencoder Training and Scoring for Robust Unsupervised Anomaly Detection in Deep Learning

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IEEE ACCESS 2020年 8卷 101824-101833页

作者： Merrill, Nicholas Eskandarian, Azim Virginia Tech Dept Mech Engn Blacksburg VA 24060 USA

The autoencoder (AE) is a fundamental deep learning approach to anomaly detection. AEs are trained on the assumption that abnormal inputs will produce higher reconstruction errors than normal ones. In practice, however, this assumption is unreliable in the unsupervised case, where the training data may contain anomalous examples. Given sufficient capacity and training time, an AE can generalize to such an extent that it reliably reconstructs anomalies. Consequently, the ability to distinguish anomalies via reconstruction errors is diminished. We respond to this limitation by introducing three new methods to more reliably train AEs for unsupervised anomaly detection: cumulative error scoring (CES), percentile loss (PL), and early stopping via knee detection. We demonstrate significant improvements over conventional AE training on image, remote-sensing, and cybersecurity datasets.

关键词： Training Anomaly detection Image reconstruction Machine learning Training data Reliability Data models Hyperspectral HSI deep learning anomaly detection unsupervised autoencoder outlier detection condition monitoring

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A fusion autoencoder model and piecewise anomaly index for aero-engine fault diagnosis

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APPLIED INTELLIGENCE 2024年第20期54卷 10148-10160页

作者： Feng, Kun Xiao, Yuan Li, Zhouzheng Miao, Dongyan Beijing Univ Chem Technol State Key Lab High end Compressor & Syst Technol 15 Beisanhuan East Rd Beijing 100029 Peoples R China

Safety, efficiency, and reliability are essential requirements for aero-engines. Timely and accurate diagnosis of engine faults enables effective planning of maintenance operations and reduces downtime. Although traditional physics-based methods perform well under controlled test bench scenarios, their effectiveness in handling very noisy data and missing values is limited, constraining their utility in real-world settings. To address these gaps, we propose a fusion autoencoder that combines physics-informed and pattern-informed techniques, augmented with a Beta-Variational autoencoder learning backbone to enhance the robustness of the model. Additionally, a novel health index called the piecewise anomaly index is proposed that can detect and classify faults simultaneously. To evaluate the efficacy of the novel framework, we modified the New Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS) dataset to simulate real-world scenarios and conducted experiments. The results show that the proposed method can detect faults earlier than common techniques, while also achieving accurate fault classification and degree determination with the new index.

关键词： Aero-engine Fault diagnosis autoencoder Health index

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SBHA: Sensitive Binary Hashing autoencoder for Image Retrieval

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IEEE TRANSACTIONS ON CYBERNETICS 2024年第7期54卷 3954-3967页

作者： Wang, Ting Lu, Su Zhang, Jianjun Liu, Xuyu Tian, Xing Ng, Wing W. Y. Chen, Wei-neng South China Univ Technol Guangzhou Peoples Hosp 1 Sch Med Dept Radiol Guangzhou 510006 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangdong Prov Key Lab Computat Intelligence & Cyb Guangzhou 510006 Peoples R China South China Normal Univ Sch Artificial Intelligence Guangzhou 510631 Peoples R China

Binary hashing is an effective approach for content-based image retrieval, and learning binary codes with neural networks has attracted increasing attention in recent years. However, the training of hashing neural networks is difficult due to the binary constraint on hash codes. In addition, neural networks are easily affected by input data with small perturbations. Therefore, a sensitive binary hashing autoencoder (SBHA) is proposed to handle these challenges by introducing stochastic sensitivity for image retrieval. SBHA extracts meaningful features from original inputs and maps them onto a binary space to obtain binary hash codes directly. Different from ordinary autoencoders, SBHA is trained by minimizing the reconstruction error, the stochastic sensitive error, and the binary constraint error simultaneously. SBHA reduces output sensitivity to unseen samples with small perturbations from training samples by minimizing the stochastic sensitive error, which helps to learn more robust features. Moreover, SBHA is trained with a binary constraint and outputs binary codes directly. To tackle the difficulty of optimization with the binary constraint, we train the SBHA with alternating optimization. Experimental results on three benchmark datasets show that SBHA is competitive and significantly outperforms state-of-the-art methods for binary hashing.

关键词： autoencoder binary hashing image retrieval stochastic sensitivity

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Monocular depth estimation with multi-view attention autoencoder

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第23期81卷 33759-33770页

作者： Jung, Geunho Yoon, Sang Min Kookmin Univ Coll Comp Sci HCI Lab 77 Jeongneung Ro Seoul 02707 South Korea

Depth map estimation from a single RGB image is a fundamental computer vision and image processing task for various applications. Deep learning based depth map estimation has improved prediction accuracy compared with traditional approaches by learning huge numbers of RGB-D images, but challenging issues remain for distorted and blurry reconstruction in object boundaries because the features are not enforced during training. This paper presents a multi-view attention autoencoder embedded in a deep neural network to emphasize self-representative features, which provide robust depth maps by simultaneously accentuating useful features and reducing redundant features to improve depth map estimation performance. Qualitative and quantitative experiments were conducted to verify the proposed network effectiveness, which can be utilized for three-dimensional scene reconstruction and understanding.

关键词： Depth estimation autoencoder Attention module

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Denoised Internal Models:A Brain-inspired autoencoder Against Adversarial Attacks

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Machine Intelligence Research 2022年第5期19卷 456-471页

作者： Kai-Yuan Liu Xing-Yu Li Yu-Rui Lai Hang Su Jia-Chen Wang Chun-Xu Guo Hong Xie Ji-Song Guan Yi Zhou School of Life Sciences and Technology ShanghaiTech UniversityShanghai 201210China Shanghai Center for Brain Science and Brain-inspired Technology Shanghai 201602China School of Life Sciences Tsinghua UniversityBeijing 100084China National Engineering Laboratory for Brain-inspired Intelligence Technology and Application School of Information Science and TechnologyUniversity of Science and Technology of ChinaHefei 230026China Institute of Photonic Chips University of Shanghai for Science and TechnologyShanghai 200093China Centre for Artificial-intelligence Nanophotonics School of Optical-electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China

Despite its great success,deep learning severely suffers from robustness;i.e.,deep neural networks are very vulnerable to adversarial attacks,even the simplest *** by recent advances in brain science,we propose the denoised internal models(DIM),a novel generative autoencoder-based model to tackle this *** the pipeline in the human brain for visual signal processing,DIM adopts a two-stage *** the first stage,DIM uses a denoiser to reduce the noise and the dimensions of inputs,reflecting the information pre-processing in the *** by the sparse coding of memory-related traces in the primary visual cortex,the second stage produces a set of internal models,one for each *** evaluate DIM over 42 adversarial attacks,showing that DIM effectively defenses against all the attacks and outperforms the SOTA on the overall robustness on the MNIST(Modified National Institute of Standards and Technology)dataset.

关键词： Brain-inspired learning autoencoder robustness adversarial attack generative model

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DDANF: Deep denoising autoencoder normalizing flow for unsupervised multivariate time series anomaly detection

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ALEXANDRIA ENGINEERING JOURNAL 2024年 108卷 436-444页

作者： Zhao, Xigang Liu, Peng Mahmoudi, Said Garg, Sahil Kaddoum, Georges Hassan, Mohammad Mehedi Hangzhou Dianzi Univ Sch Comp Sci & Technol Hangzhou 310018 Peoples R China Univ Mons Fac Engn Comp Sci Dept B-7000 Mons Belgium Ecole Technol Super Montreal PQ H3C 1K3 Canada Chitkara Univ Chitkara Univ Inst Engn & Technol Ctr Res Impact & Outcome Rajpura 140401 Punjab India King Saud Univ Coll Comp & Informat Sci Dept Informat Syst Riyadh 11543 Saudi Arabia

In recent years, the proliferation of IoT technologies and the widespread adoption of wireless sensors across various critical infrastructures such as power plants, service monitoring systems, space and earth exploration missions, and water treatment facilities have resulted in the generation of vast quantities of multivariate time series data. Within this context, unsupervised anomaly detection has emerged as a pivotal yet challenging problem in time series research, necessitating machine learning models capable of identifying rare anomalies amidst massive datasets. Traditionally, unsupervised methods have approached this issue by learning representations of primary patterns within sequences and detecting deviations through reconstruction errors. However, the effectiveness of this approach is often limited due to the intricate dynamics and diverse patterns inherent in these dynamic systems. Moreover, many existing unsupervised anomaly detection techniques fail to fully exploit inter-feature relationships within multivariate time series data, thereby overlooking a crucial criterion for accurate detection. To address these shortcomings, this paper introduces a novel unsupervised method for multivariate time series anomaly detection based on normalized flows and autoencoders. Central to our approach is the incorporation of a channel shuffling mechanism during training, enhancing the model's capacity to discern inter-channel patterns and anomalies. Concurrently, the application of normalized flows within the autoencoder framework serves to constrain the latent space, effectively isolating anomalies and improving detection accuracy. Experimental validation conducted on two large-scale public datasets demonstrates the efficacy of the proposed method compared to established benchmarks, highlighting its superior performance.

关键词： Anomaly detection Normalizing flow Time series autoencoder

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