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Lifted Bregman Training of Neural Networks

JOURNAL OF MACHINE LEARNING RESEARCH 2023年第1期24卷 1-51页

作者： Wang, Xiaoyu Benning, Martin Univ Cambridge Dept Appl Math & Theoret Phys Cambridge CB3 0WA England Queen Mary Univ London Sch Math Sci London E1 4NS England

We introduce a novel mathematical formulation for the training of feed-forward neural networks with (potentially non-smooth) proximal maps as activation functions. This formulation is based on Bregman distances and a key advantage is that its partial derivatives with respect to the network's parameters do not require the computation of derivatives of the network's activation functions. Instead of estimating the parameters with a combination of first-order optimisation method and back-propagation (as is the state-of-the-art), we propose the use of non-smooth first-order optimisation methods that exploit the specific structure of the novel formulation. We present several numerical results that demonstrate that these training approaches can be equally well or even better suited for the training of neural network-based classifiers and (denoising) autoencoders with sparse coding compared to more conventional training frameworks.

关键词： Lifted network training distributed optimisation Bregman distances sparse autoencoder denoising autoencoder classification compressed sensing

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A novel U-Net with dense block for drum signal separation from polyphonic music signal mixture

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SIGNAL IMAGE AND VIDEO PROCESSING 2023年第3期17卷 627-633页

作者： George, E. Vinitha Devassia, V. P. Cochin Univ Sci & Technol Model Engn Coll Kochi Kerala India St Josephs Coll Engn & Technol Palai Kerala India

Deep neural network algorithms have shown promising results for music source signal separation. Most existing methods rely on deep networks, where billions of parameters need to be trained. In this paper, we propose a novel autoencoder framework with a reduced number of parameters to separate the drum signal component from a music signal mixture. A denoising autoencoder with a U-Net architecture and direct skip connections was employed. A dense block is included in the bottleneck of the autoencoder stage. This technique was tested on both demixing secret data (DSD) and the MUSDB database. The source-to-distortion ratio (SDR) for the proposed method was at par with that of other state-of-the-art methods, whereas the number of parameters required was quite low, making it computationally more efficient. The experiment performed using the proposed method to separate drum signal yielded an average SDR of 5.71 on DSD and 6.45 on MUSDB database while using only 0.32 million parameters.

关键词： Audio source separation Convolutional neural network Deep learning denoising autoencoder U-Net architecture

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Enhancing Context-Aware Recommendation Using Trust-Based Contextual Attentive autoencoder

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NEURAL PROCESSING LETTERS 2023年第5期55卷 6843-6864页

作者： Abinaya, S. Alphonse, A. Sherly Abirami, S. Kavithadevi, M. K. Vellore Inst Technol Sch Comp Sci & Engn Chennai Tamil Nadu India Thiagarajar Coll Engn Dept Comp Sci & Engn Madurai Tamil Nadu India

Context-aware recommender systems are intended primarily to consider the circumstances under which a user encounters an item to provide better-personalized recommendations. Users acquire point-of-interest, movies, products, and various online resources as suggestions. Classical collaborative filtering algorithms are shown to be satisfactory in a variety of recommendation activities processes, but cannot often capture complicated interactions between item and user, along with sparsity and cold start constraints. Hence it becomes a surge to apply a deep learning-based recommender model owing to its dynamic modeling potential and sustained success in other fields of application. In this work, a trust-based attentive contextual denoising autoencoder (TACDA) for enhanced Top-N context-aware recommendation is proposed. Specifically, the TCADA model takes the sparse preference of the user that is integrated with trust data as input into the autoencoder to prevail over the cold start and sparsity obstacle and efficiently accumulates the context condition into the model via attention framework. Thereby, the attention technique is used to encode context features into a latent space of the user's trust data that is integrated with their preferences, which interconnects personalized context circumstances with the active user's choice to deliver recommendations suited to that active user. Experiments conducted on Epinions, Caio, and LibraryThing datasets make it obvious the efficiency of the TACDA model persistently outperforms the state-of-the-art methods.

关键词： Social networks Attention mechanism denoising autoencoder Context-aware recommendations

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Wavelet Integrated Convolutional Neural Network for ECG Signal denoising 31st

Wavelet Integrated Convolutional Neural Network for ECG Sign...

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31st International Conference on Multimedia Modeling

作者： Terada, Takamasa Toyoura, Masahiro Univ Yamanashi 4-3-11 Takeda Kofu Yamanashi 4008511 Japan

ISBN: (纸本)9789819620708;9789819620715

Wearable electrocardiogram (ECG) measurement using dry electrodes has a problem with high-intensity noise distortion. Hence, a robust noise reduction method is required. However, overlapping frequency bands of ECG and noise make noise reduction difficult. Hence, it is necessary to provide a mechanism that changes the characteristics of the noise based on its intensity and type. This study proposes a convolutional neural network (CNN) model with an additional wavelet transform layer that extracts the specific frequency features in a clean ECG. Testing confirms that the proposed method effectively predicts accurate ECG behavior with reduced noise by accounting for all frequency domains. In an experiment, noisy signals in the signal-to-noise ratio (SNR) range of -10-10 are evaluated, demonstrating that the efficiency of the proposed method is higher when the SNR is small.

关键词： Electrocardiogram Wavelet transform denoising autoencoder Convolutional neural network

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Microscopic biopsy image reconstruction using inception block with denoising auto-encoder approach

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International Journal of Information Technology (Singapore) 2024年第4期16卷 2413-2423页

作者： Singh, Shiksha Kumar, Rajesh Department of Electronics & Communication JK Institute of Applied Physics & Technology University of Allahabad UP Prayagraj India

The use of computer-aided image analysis for disease diagnosis and prognosis has dramatically increased during the past 10 years. The introduction of computer-assisted image analysis of images produced by equipment employed in medical science for the prognosis of various diseases has been made possible by the development of digital scanners and high-resolution imaging technologies. Additionally, much like any other digital image, the obtained medical images may become distorted by various distortions and sounds. The noise in histopathology images can be random or have an even frequency distribution;it can be introduced during data acquisition by imaging devices or as a result of signal processing algorithms;either way, the noise appears in the digital image in an uncorrelated way, inevitably lowering the visual quality of the images. In the present article, employed an integrative strategy for the image denoising of the histopathological images of Breast cancer through deep learning (DL) approach. Moreover, some noises remain intact while following the approach due to the direct conduction of the feature map and also the level noise. Henceforth, a denoising model using denoising autoencoder with an Inception network block (InDAENET) has been proposed to deal with the left-out feature maps and high noise signals related to the medical images. The proposed method has been prepared and verified on the publicly available dataset BreakHis. This approach has further been successfully compared with the conventional denoising autoencoder (DAE) method. Henceforth, it paves the way for Histopathological Image Reconstruction using an Inception block with denoising autoencoder for Breast Cancer Detection. © 2024, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Convolutional neural network denoising autoencoder Histopathological image Inception network Medical image

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A Collaborative Framework Using Multimodal Data and Adaptive Noise for Human Behavior Anomaly Detection

A Collaborative Framework Using Multimodal Data and Adaptive...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yang, Guoqing Gao, Jianzhe Zhang, Kejia He, Yifan Luo, Zhiming Li, Shaozi Xiamen Univ Dept Artif Intelligence Xiamen Peoples R China Wuyi Univ Fujian Key Lab Big Data Applicat & Intellectualiz Wuyishan Peoples R China

ISBN: (纸本)9798350359329;9798350359312

Human behavior anomaly detection in video aims to identify unusual behaviors that are crucial for public safety. Recently, there has been an increase in reconstruction or predictionbased methods that integrate diverse modal features to enhance anomaly detection. However, they use methods that independently or directly fusion multimodal features without fully considering the collaborative potential between multimodal features, which are susceptible to interference from semantic differences, thereby impacting detection performance. In contrast, we design a collaborative framework using multimodal data and adaptive noise for behavior anomaly detection. Our framework detects anomalies by analyzing the contrastive differences between two modalities alongside single-frame reconstruction errors. Specifically, we first learn the correlation between RGB and skeletal modalities for normal behavior through contrastive learning and use inter-modal contrast difference to detect motion anomalies. Additionally, we propose a single-frame reconstruction network that adaptively adds noise based on the importance of foreground features to detect appearance anomalies. Anomalies often occur in the motion foreground, and increasing noise in this area can make it more difficult to reconstruct anomalies. Extensive experiments validate the state-of-the-art performance of our method on three public datasets.

关键词： Human behavior anomaly detection Multimodal feature Contrastive learning denoising autoencoder

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MIDIA: exploring denoising autoencoders for missing data imputation

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DATA MINING AND KNOWLEDGE DISCOVERY 2020年第6期34卷 1859-1897页

作者： Ma, Qian Lee, Wang-Chien Fu, Tao-Yang Gu, Yu Yu, Ge Dalian Maritime Univ Coll Informat Sci & Technol Dalian Peoples R China Penn State Univ Dept Comp Sci & Engn State Coll PA 16801 USA Northeastern Univ Sch Engn & Comp Sci Shenyang Peoples R China

Due to the ubiquitous presence of missing values (MVs) in real-world datasets, the MV imputation problem, aiming to recover MVs, is an important and fundamental data preprocessing step for various data analytics and mining tasks to effectively achieve good performance. To impute MVs, a typical idea is to explore the correlations amongst the attributes of the data. However, those correlations are usually complex and thus difficult to identify. Accordingly, we develop a new deep learning model calledMIssing Data Imputation denoising autoencoder(MIDIA) that effectively imputes the MVs in a given dataset by exploring non-linear correlations between missing values and non-missing values. Additionally, by considering various data missing patterns, we propose two effective MV imputation approaches based on the proposed MIDIA model, namely MIDIA-Sequential and MIDIA-Batch. MIDIA-Sequential imputes the MVs attribute-by-attribute sequentially by training an independent MIDIA model for each incomplete attribute. By contrast, MIDIA-Batch imputes the MVs in one batch by training a uniform MIDIA model. Finally, we evaluate the proposed approaches by experimentation in comparison with existing MV imputation algorithms. The experimental results demonstrate that both MIDIA-Sequential and MIDIA-Batch achieve significantly higher imputation accuracy compared with existing solutions, and the proposed approaches are capable of handling various data missing patterns and data types. Specifically, MIDIA-Sequential performs better than MIDIA-Batch for data with monotone missing pattern, while MIDIA-Batch performs better than MIDIA-Sequential for data with general missing pattern.

关键词： Missing data imputation denoising autoencoder MIDIA Deep learning

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Examining the Mapping Functions of denoising autoencoders in Singing Voice Separation

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2020年 28卷 266-278页

作者： Mimilakis, Stylianos Ioannis Drossos, Konstantinos Cano, Estefania Schuller, Gerald Fraunhofer IDMT Semant Mus Technol Grp D-98693 Ilmenau Germany Tampere Univ Audio Res Grp Tampere 33720 Finland Tech Univ Ilmenau Inst Media Technol D-98693 Ilmenau Germany

The goal of this article is to investigate what singing voice separation approaches based on neural networks learn from the data. We examine the mapping functions of neural networks based on the denoising autoencoder (DAE) model that are conditioned on the mixture magnitude spectra. To approximate the mapping functions, we propose an algorithm inspired by the knowledge distillation, denoted the neural couplings algorithm (NCA). The NCA yields a matrix that expresses the mapping of the mixture to the target source magnitude information. Using the NCA, we examine the mapping functions of three fundamental DAE-based models in music source separation;one with single-layer encoder and decoder, one with multi-layer encoder and single-layer decoder, and one using skip-filtering connections (SF) with a single-layer encoding and decoding. We first train these models with realistic data to estimate the singing voice magnitude spectra from the corresponding mixture. We then use the optimized models and test spectral data as input to the NCA. Our experimental findings show that approaches based on the DAE model learn scalar filtering operators, exhibiting a predominant diagonal structure in their corresponding mapping functions, limiting the exploitation of inter-frequency structure of music data. In contrast, skip-filtering connections are shown to assist the DAE model in learning filtering operators that exploit richer inter-frequency structures.

关键词： Source separation Computational modeling Noise reduction Neural networks Approximation algorithms Multiple signal classification Decoding Music source separation singing voice denoising autoencoder DAE skip connections neural couplings algorithm NCA

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Recognition of mild-to-moderate depression based on facial expression and speech 24

Recognition of mild-to-moderate depression based on facial e...

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5th International Conference on Computing, Networks and Internet of Things (CNIOT)

作者： Li, Jinlong Li, Ying Lanzhou Inst Technol Coll Elect Informat Engn Lanzhou Peoples R China

ISBN: (纸本)9798400716751

The behavioral symptoms of patients with mild to moderate depression (MMD) are usually not obvious enough, which poses a challenge to MMD recognition research. A three-level feature construction strategy for facial expression was proposed to fully characterize the differences in facial activity between MMD patients and healthy control groups (HCs). Level 1: Construct geometric features to describe facial activity preliminarily. Level 2: Input geometric features into a denoising autoencoder (DAE) to generate a new hidden layer representation. Level 3: Gaussian mixture model (GMM) further characterizes the hidden layer features. Meanwhile, a speech feature set was constructed based on the fundamental frequency (F0), pitch intensity, mel frequency cepstral coefficients (MFCCs), and syllable pauses of speech. Finally, facial expression and speech were fused at the feature layer, and MMD recognition was carried out based on four classic classification algorithms. The experimental results show that the MMD recognition accuracy of the male and female groups can reach 70.3% and 68.3%, respectively.

关键词： Depression recognition Facial expression Speech denoising autoencoder

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Harmless Overfitting: Using denoising autoencoders in Estimation of Distribution Algorithms

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JOURNAL OF MACHINE LEARNING RESEARCH 2020年第1期21卷 1-31页

作者： Probst, Malte Rothlauf, Franz Honda Res Inst EU Carl Legien Str 30 D-63073 Offenbach Germany Johannes Gutenberg Univ Mainz Informat Syst & Business Adm Jakob Welder Weg 9 D-55128 Mainz Germany Johannes Gutenberg Univ Mainz Mainz Germany

Estimation of Distribution Algorithms (EDAs) are metaheuristics where learning a model and sampling new solutions replaces the variation operators recombination and mutation used in standard Genetic Algorithms. The choice of these models as well as the corresponding training processes are subject to the bias/variance tradeoff, also known as under- and overfitting: simple models cannot capture complex interactions between problem variables, whereas complex models are susceptible to modeling random noise. This paper suggests using denoising autoencoders (DAEs) as generative models within EDAs (DAE-EDA). The resulting DAE-EDA is able to model complex probability distributions. Furthermore, overfitting is less harmful, since DAEs overfit by learning the identity function. This overfitting behavior introduces unbiased random noise into the samples, which is no major problem for the EDA but just leads to higher population diversity. As a result, DAE-EDA runs for more generations before convergence and searches promising parts of the solution space more thoroughly. We study the performance of DAE-EDA on several combinatorial single-objective optimization problems. In comparison to the Bayesian Optimization Algorithm, DAE-EDA requires a similar number of evaluations of the objective function but is much faster and can be parallelized efficiently, making it the preferred choice especially for large and difficult optimization problems.

关键词： denoising autoencoder estimation of distribution algorithm overfitting combinatorial optimization neural networks

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