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autoencoder-based self-supervised hashing for cross-modal retrieval

MULTIMEDIA TOOLS AND APPLICATIONS 2021年第11期80卷 17257-17274页

作者： Li, Yifan Wang, Xuan Cui, Lei Zhang, Jiajia Huang, Chengkai Luo, Xuan Qi, Shuhan Harbin Inst Technol Shenzhen Comp Sci & Technol Shenzhen Peoples R China

Cross-modal retrieval has gained lots of attention in the era of the multimedia data explosion. Taking advantage of low storage cost and fast retrieval speed, hash learning-based methods become more and more popular in this field. The crucial bottlenecks of cross-modal retrieval are twofold: the heterogeneous gap in different modalities and the semantic gap among similar data with various modalities. To address these issues, we adopt self-supervised fashion to bridge the heterogeneous gap by generating the cohesive features of different instances. To mitigate the semantic gap, we use triplet sampling to optimize the semantic loss in inter-modal and intra-modal, which increase the discriminability of our approach. Experimental on two benchmark datasets show the efficiency and robustness of our method, and the extended experiments show the scalability.

关键词： Cross-modal retrieval Hash learning autoencoder Self-supervised

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autoencoder With Emotion Embedding for Speech Emotion Recognition

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IEEE ACCESS 2021年 9卷 51231-51241页

作者： Zhang, Chenghao Xue, Lei Shanghai Univ Sch Commun & Informat Engn Shanghai 200444 Peoples R China

An important part of the human-computer interaction process is speech emotion recognition (SER), which has been receiving more attention in recent years. However, although a wide diversity of methods has been proposed in SER, these approaches still cannot improve the performance. A key issue in the low performance of the SER system is how to effectively extract emotion-oriented features. In this paper, we propose a novel algorithm, an autoencoder with emotion embedding, to extract deep emotion features. Unlike many previous works, instance normalization, which is a common technique in the style transfer field, is introduced into our model rather than batch normalization. Furthermore, the emotion embedding path in our method can lead the autoencoder to efficiently learn a priori knowledge from the label. It can enable the model to distinguish which features are most related to human emotion. We concatenate the latent representation learned by the autoencoder and acoustic features obtained by the openSMILE toolkit. Finally, the concatenated feature vector is utilized for emotion classification. To improve the generalization of our method, a simple data augmentation approach is applied. Two publicly available and highly popular databases, IEMOCAP and EMODB, are chosen to evaluate our method. Experimental results demonstrate that the proposed model achieves significant performance improvement compared to other speech emotion recognition systems.

关键词： Feature extraction Speech recognition Emotion recognition Spectrogram Noise reduction Hidden Markov models Acoustics Speech emotion recognition autoencoder emotion embedding instance normalization

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autoencoder-Combined Generative Adversarial Networks for Synthetic Image Data Generation and Detection of Jellyfish Swarm

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IEEE ACCESS 2018年 6卷 54207-54214页

作者： Kim, Kyukwang Myung, Hyun Korea Adv Inst Sci & Technol Urban Robot Lab Daejeon 34141 South Korea

Image-based sensing of jellyfish is important as they can cause great damage to the fisheries and seaside facilities and need to be properly controlled. In this paper, we present a deep-learning-based technique to generate a synthetic image of the jellyfish easily with autoencoder-combined generative adversarial networks. The proposed system can easily generate simple images with a smaller number of data sets compared with other generative networks. The generated output showed high similarity with the real-image data set. The application using a fully convolutional network and regression network to estimate the size of the jellyfish swarm was also demonstrated, and showed high accuracy during the estimation test.

关键词： autoencoder generative adversarial networks jellyfish swarm fully convolutional network regression

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autoencoder framework based on orthogonal projection constraints improves anomalies detection

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NEUROCOMPUTING 2021年 450卷 372-388页

作者： Yu, Qien Kavitha, Muthusubash Kurita, Takio Hiroshima Univ Dept Informat Engn Higashihiroshima Hiroshima 7398521 Japan Hiroshima Univ Grad Sch Adv Sci & Engn Higashihiroshima Hiroshima 7398521 Japan Nagasaki Univ Sch Informat & Data Sci Nagasaki Japan

In this study, we propose a novel autoencoder framework based on orthogonal projection constraint (OPC) for anomaly detection (AD) on both complex image and vector datasets. Orthogonal projection is useful to capture the null subspace that consists of noisy information for AD, which is explicitly ignored in the existing approaches. The exploration of double subspaces, called normal space (NS) and abnormal space (AS) can improve the discriminative manifold information. Therefore, in this study, autoencoder framework based on the OPC learning method is proposed that combines the orthogonal subspace score and the reconstruction error score in the target tasks for AD. To the best of our knowledge, this is the first study that introduces an autoencoder-based model with two orthogonal subspaces for AD. Through the orthogonality, the anomaly-free data and abnormalnnosiy information are projected into the NS and the AS, respectively. Thus, it potentially addresses the problem of the distribution of generative model by combining the abilities of two subspaces that can appropriately learn the features and establish a strict boundaries around the normal data. For image datasets, we propose a convolutional autoencoder based on OPC. Additionally, the generalization and adaptability of the proposed method in AD was investigated using vector datasets by implementing a fully-connected layer-based OPC in the encoder-decoder structure. The effectiveness of the proposed framework for AD was evaluated through the comparison with state-of-the-art approaches. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Orthogonal projection autoencoder Anomaly detection Subspace detection

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autoencoder With Invertible Functions for Dimension Reduction and Image Reconstruction

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2018年第7期48卷 1065-1079页

作者： Yang, Yimin Wu, Q. M. Jonathan Wang, Yaonan Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China

The extreme learning machine (ELM), which was originally proposed for "generalized" single-hidden layer feed-forward neural networks, provides efficient unified learning solutions for the applications of regression and classification. Although, it provides promising performance and robustness and has been used for various applications, the single-layer architecture possibly lacks the effectiveness when applied for natural signals. In order to over come this shortcoming, the following work indicates a new architecture based on multilayer network framework. The significant contribution of this paper are as follows: 1) unlike existing multilayer ELM, in which hidden nodes are obtained randomly, in this paper all hidden layers with invertible functions are calculated by pulling the network output back and putting it into hidden layers. Thus, the feature learning is enriched by additional information, which results in better performance;2) in contrast to the existing multilayer network methods, which are usually efficient for classification applications, the proposed architecture is implemented for dimension reduction and image reconstruction;and 3) unlike other iterative learning-based deep networks (DL), the hidden layers of the proposed method are obtained via four steps. Therefore, it has much better learning efficiency than DL. Experimental results on 33 datasets indicate that, in comparison to the other existing dimension reduction techniques, the proposed method performs competitively better with fast training speeds.

关键词： autoencoder deep learning (DL) dimension reduction extreme learning machine (ELM) feature selection generalization performance

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autoencoder-Based Eggshell Crack Detection Using Acoustic Signal

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JOURNAL OF FOOD PROCESS ENGINEERING 2024年第11期47卷

作者： Yabanova, Ismail Balci, Zekeriya Yumurtaci, Mehmet Unler, Tarik Manisa Celal Bayar Univ HFT Technol Fac Elect Engn Dept Manisa Turkiye Van Yuzuncu Yil Univ Caldiran Vocat Sch Elect & Automat Dept Van Turkiye Afyon Kocatepe Univ Dept Elect & Elect Engn Afyon Turkiye Necmettin Erbakan Univ Aeronaut & Astronaut Fac Avionics Dept Konya Turkiye

Breaks or cracks in eggshells offer substantial food safety issues. Bacteria and viruses, in particular, are more likely to enter the egg through breaks and cracks, increasing the risk of food poisoning. Furthermore, deformations in the shell may compromise the integrity of the protective shell, exposing the egg to more external variables and causing it to lose freshness and decay faster. To reduce such hazards, this research created an innovative crack detection system based on an autoencoder (AE) that uses acoustic signals from eggshells. A system that creates an acoustic effect by hitting the eggshell without damaging it was designed, and these effects were recorded through a microphone. Acoustic signal data of size 1 x 1000 was fed into k nearest neighbor (kNN), decision tree (DT), and support vector machine (SVM) classifiers. AE was employed to reduce data size in order to accommodate the raw data's unique features. This AE model, which reduces data size, was used with many classifiers and was able to accurately distinguish between intact and cracked eggs. The built AE-based classifier model completed the classification procedure with 100% accuracy, including microcracks that are invisible to the naked eye.

关键词： acoustic signal autoencoder classification crack eggshell

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autoencoder-Based Recommender System Exploiting Natural Noise Removal

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IEEE ACCESS 2023年 11卷 30609-30618页

作者： Park, Hyeseong Jeong, Jaeik Oh, Kyung-Whan Kim, Hongseok Sogang Univ Dept Comp Sci & Engn Seoul 04107 South Korea Sogang Univ Dept Elect Engn Seoul 04107 South Korea

Collaborative filtering (CF) is a widely used technique in recommender systems by automatically predicting the user's latent interests based on many users' historical rating data. To improve the performance of the CF-based recommender systems, users' rating data should be pre-processed to avoid noise and enhance data reliability. Many researchers studied anomaly detection to remove malicious noise caused by shilling attacks, but anomalies can still exist in non-attacked real user data, which is called natural noise, as the ratings of users can be impacted by unpredictable factors such as other users' ratings and anchoring bias. In this paper, we propose an autoencoder-based recommendation system for exploiting the ability of both anomaly detection and CF. The proposed system detects the natural noise in the rating data based on the reconstruction errors after training. By removing the detected natural noise, CF can predict the unrated ratings with noise-free data. Our experiments show that the proposed model showed better performance than the traditional method by reducing the error by up to 5% compared to the method that does not consider natural noise detection and reducing the error by up to 4% compared to the conventional rating classification based natural noise detection methods.

关键词： Recommender systems Encoding Training Anomaly detection Decoding Prediction algorithms Feature extraction Collaborative filtering recommender system natural noise autoencoder anomaly detection

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autoencoder-Based MIMO Cooperative Communications With Quantize-Forward Relaying

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第12期73卷 19229-19239页

作者： Shin, Juin Jin, Xianglan Jeonbuk Natl Univ Div Elect Engn Jeonju 54896 South Korea Jeonbuk Natl Univ IT Convergence Res Ctr Jeonju 54896 South Korea

This paper investigates an autoencoder-based quantize-forward (QF) relay system that includes a source, a destination, and a relay, each equipped with multiple antennas. The existing phase quantization (PQ) algorithm at the relay has limitations in capturing the amplitude differences of received signals, leading to performance saturation with increasing quantization bits. To address these limitations, we propose a novel relay algorithm, amplitude-phase quantization (APQ), which quantizes both the phase and the amplitude. Moreover, we introduce neural networks into the relay process, resulting in PQ with neural networks (PQNN) and APQ with neural networks (APQNN), which is expected to further improve system performance at the expense of additional computational load at the relay. We also propose a sub-message one-hot encoding method and a retraining approach for the worst-performing sub-message to reduce computational complexity and improve performance in autoencoder-based systems. Simulation results demonstrate that the autoencoder-based QF relay system, with various relay algorithms and the sub-message one-hot encoding method, achieves excellent performance with reduced memory usage at the relay and significantly reduced complexity at the source and destination.

关键词： Relays Quantization (signal) Neural networks Communication systems System performance Encoding MIMO communication autoencoder deep learning multiple-input multiple-output (MIMO) phase quantization relay

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autoencoder asset pricing models

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JOURNAL OF ECONOMETRICS 2021年第1期222卷 429-450页

作者： Gu, Shihao Kelly, Bryan Xiu, Dacheng Univ Chicago Booth Sch Business Chicago IL 60637 USA Yale Univ AQR Capital Management New Haven CT 06520 USA NBER Cambridge MA 02138 USA

We propose a new latent factor conditional asset pricing model. Like Kelly, Pruitt, and Su (KPS, 2019), our model allows for latent factors and factor exposures that depend on covariates such as asset characteristics. But, unlike the linearity assumption of KPS, we model factor exposures as a flexible nonlinear function of covariates. Our model retrofits the workhorse unsupervised dimension reduction device from the machine learning literature - autoencoder neural networks - to incorporate information from covariates along with returns themselves. This delivers estimates of nonlinear conditional exposures and the associated latent factors. Furthermore, our machine learning framework imposes the economic restriction of no-arbitrage. Our autoencoder asset pricing model delivers out-of-sample pricing errors that are far smaller (and generally insignificant) compared to other leading factor models. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Stock returns Conditional asset pricing model Nonlinear factor model Machine learning autoencoder Neural networks Big data

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autoencoder embedded dictionary learning for nonlinear industrial process fault diagnosis

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JOURNAL OF PROCESS CONTROL 2021年 101卷 24-34页

作者： Li, Yanxia Chai, Yi Yin, Hongpeng Chongqing Univ Coll Automat Chongqing 400044 Peoples R China State Key Lab Power Transmiss Equipment & Syst Se Chongqing 400030 Peoples R China

Industrial processes usually exhibit great nonlinearity generated from the effects of complex mechanisms, system integrations and multiple working conditions. Although a variety of dictionary learning algorithms have been proposed in recent years for industrial process fault diagnosis, most of them only model the process data via a linear combination of a few dictionary atoms, which cannot effectively characterize the nonlinear relationships among variables and may lead to limited diagnosis performance. Recent improvements in multilayer neural networks, especially the autoencoders, offer opportunities to tackle the nonlinear problem. However, the overall limited availability of fault samples poses great challenges in achieving satisfactory performance. To address the mentioned issues simultaneously, the present study proposes an autoencoder Embedded Dictionary Learning approach (AEDL) for nonlinear industrial process fault diagnosis. First, an autoencoder is employed to learn a nonlinear mapping that maps the linearly inseparable industrial process data to a high-dimensional space, where a desired dictionary is learned according to the basic dictionary learning algorithm. Next, two supervised graphs, leveraging the priors of industrial process data, are introduced into the learning process to make the proposed approach robust to training samples. After obtaining the dictionary, the coding coefficients of the process data over the dictionary can be used for fault diagnosis via a simple classifier. As revealed from the encouraging experimental results on the Tennessee Eastman process, the developed approach outperforms several dictionary learning approaches and some other nonlinear fault diagnosis methods. (C) 2021 Published by Elsevier Ltd.

关键词： Fault diagnosis Nonlinear industrial process Dictionary learning autoencoder

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