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Harmless Overfitting: Using denoising autoencoders in Estimation of Distribution Algorithms

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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Semisupervised learning using denoising autoencoders for brain lesion detection and segmentation

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JOURNAL OF MEDICAL IMAGING 2017年第4期4卷 041311页

作者： Alex, Varghese Vaidhya, Kiran Thirunavukkarasu, Subramaniam Kesavadas, Chandrasekharan Krishnamurthi, Ganapathy Indian Inst Technol Madras Dept Engn Design Madras Tamil Nadu India Sree Chitra Tirunal Inst Med Sci & Technol Dept Radiol Trivandrum Kerala India

The work explores the use of denoising autoencoders (DAEs) for brain lesion detection, segmentation, and false-positive reduction. Stacked denoising autoencoders (SDAEs) were pretrained using a large number of unlabeled patient volumes and fine-tuned with patches drawn from a limited number of patients (n = 20, 40, 65). The results show negligible loss in performance even when SDAE was fine-tuned using 20 labeled patients. Low grade glioma (LGG) segmentation was achieved using a transfer learning approach in which a network pretrained with high grade glioma data was fine-tuned using LGG image patches. The networks were also shown to generalize well and provide good segmentation on unseen BraTS 2013 and BraTS 2015 test data. The manuscript also includes the use of a single layer DAE, referred to as novelty detector (ND). ND was trained to accurately reconstruct nonlesion patches. The reconstruction error maps of test data were used to localize lesions. The error maps were shown to assign unique error distributions to various constituents of the glioma, enabling localization. The ND learns the nonlesion brain accurately as it was also shown to provide good segmentation performance on ischemic brain lesions in images from a different database. (c) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： brain lesion gliomas magnetic resonance imaging deep learning stacked denoising autoencoder denoising autoencoder

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Single-channel late reverberation power spectral density estimation using denoising autoencoders 19

Single-channel late reverberation power spectral density est...

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19th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2018)

作者： Kodrasi, Ina Bourlard, Herve Idiap Res Inst Speech & Audio Proc Grp Martigny Switzerland

ISBN: (纸本)9781510872219

In order to suppress the late reverberation in the spectral domain, many single-channel dereverberation techniques rely on an estimate of the late reverberation power spectral density (PSD). In this paper, we propose a novel approach to late reverberation PSD estimation using a denoising autoencoder (DA), which is trained to learn a mapping from the microphone signal PSD to the late reverberation PSD. Simulation results show that the proposed approach yields a high PSD estimation accuracy and generalizes well to unseen data. Furthermore, simulation results show that the proposed DA-based PSD estimate yields a higher PSD estimation accuracy and a similar dereverberation performance than a state-of-the-art statistical PSD estimate, which additionally also requires knowledge of the reverberation time.

关键词： late reverberation PSD denoising autoencoder dereverberation

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Radio Frequency Fingerprint Identification Based on denoising autoencoders 15

Radio Frequency Fingerprint Identification Based on Denoisin...

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15th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

作者： Yu, Jiabao Hu, Aiqun Zhou, Fen Xing, Yuexiu Yu, Yi Li, Guyue Peng, Linning Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Peoples R China Southeast Univ Sch Cyber Sci & Engn Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 210096 Peoples R China Univ Avignon CERI LIA F-84000 Avignon France Inst Super Elect Paris F-75006 Paris France

ISBN: (纸本)9781728133164

Radio Frequency Fingerprinting (RFF) is one of the promising passive authentication approaches for improving the security of the Internet of Things (IoT). However, with the proliferation of low-power IoT devices, it becomes imperative to improve the identification accuracy at low SNR scenarios. To address this problem, this paper proposes a general denoising autoencoder (DAE)-based model for deep learning RFF techniques. Besides, a partially stacking method is designed to appropriately combine the semi-steady and steady-state RFFs of ZigBee devices. The proposed Partially Stacking-based Convolutional DAE (PSC-DAE) aims at reconstructing a high-SNR signal as well as device identification. Experimental results demonstrate that compared to Convolutional Neural Network (CNN), PSCDAE can improve the identification accuracy by 14% to 23.5% at low SNRs (from -10 dB to 5 dB) under Additive White Gaussian Noise (AWGN) corrupted channels. Even at SNR = 10 dB, the identification accuracy is as high as 97.5%.

关键词： RF fingerprinting denoising autoencoder partially stacking ZigBee

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Hyperparameter Tuning to Optimize Implementations of denoising autoencoders for Imputation of Missing Spatio-temporal Data

Hyperparameter Tuning to Optimize Implementations of Denoisi...

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12th International Conference on Ambient Systems, Networks and Technologies (ANT) / 4th International Conference on Emerging Data and Industry 4.0 (EDI40)

作者： Siddiqi, Muhammad Danial Jiang, Boyuan Asadi, Reza Regan, Amelia Univ Calif Irvine Donald Bren Sch Informat & Comp Sci ICS Irvine CA 92697 USA Univ Calif Irvine Inst Transportat Studies ITS Irvine CA 92697 USA

Spatio-temporal data collected from sensors can sometimes have gaps where data is missing. Transportation planners and engineers use such data to perform various different types of analyses, but the gaps in the data make it difficult to make accurate predictions. denoising autoencoders are used to generate a cleaner version of the input signals This includes generating data for missing or dropped input signals as well. We used three different deep learning models to implement denoising autoencoders to measure each model's accuracy. Since tuning the hyperparameters can influence the accuracy of the predictions of a model, each model was optimized by focusing on four main parameters. Each of the hyperparameters had a different effect on each of the models. The simplest of the models yielded much better results than anticipated after optimizing The most complex of the models was still better, but only slightly. That slight improvement required one hundred times the computational cost required by the simplest model. Tuning the hyperparameters to optimize a simpler model can prove more beneficial than creating a more complex model that is slower and difficult to optimize (C) 2021 The Authors. Published by Elsevier B.V.

关键词： Transportation data analysis Spatio-temporal problem denoising autoencoder Missing data imputation

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Imputation of Missing Traffic Flow Data Using denoising autoencoders

Imputation of Missing Traffic Flow Data Using Denoising Auto...

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12th International Conference on Ambient Systems, Networks and Technologies (ANT) / 4th International Conference on Emerging Data and Industry 4.0 (EDI40)

作者： Jiang, Boyuan Siddiqi, Muhammad Danial Asadi, Reza Regan, Amelia Univ Calif Irvine Inst Transportat Studies ITS Irvine CA 92697 USA Univ Calif Irvine Donald Bren Sch Informat & Comp Sci ICS Irvine CA 92697 USA

In transportation engineering, spatio-temporal data including traffic flow, speed, and occupancy are collected from different kinds of sensors and used by transportation engineers for analysis. However, the missing data influence the analysis and prediction results significantly. In this paper, denoising autoencoders are used to impute the missing traffic flow data. In our earlier research, we focused on a more general situation and used three kinds of denoising autoencoders: "Vanilla", CNN, and Bi-LSTM, to impute the data with a general missing rate of 30%. The autoencoder models are used to train on data with a high missing rate of about 80% in this paper. We demonstrate that even under extreme loss conditions, and autoencoder models are very robust. By observing the hyper-parameter tuning process, the changing prediction accuracy is shown and in most cases, the three models maintain the original accuracy even under the worst situations. Moreover, the error patterns and trends concerning different sensor stations and different hours on weekdays and weekends are also visualized and analyzed. Finally, based on these results, we separate the data into weekdays and weekends, train and test the model respectively, and improve the accuracy of the imputation result significantly. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： Transportation data analysis Spatio-temporal problem denoising autoencoder Missing data imputation

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Fast Machine Fault Diagnosis Using Marginalized denoising autoencoders Based on Acoustic Signal

Fast Machine Fault Diagnosis Using Marginalized Denoising Au...

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Prognostics and System Health Management Conference (PHM-Besancon)

作者： Xiao, Dengyu Tao, Zhiyu Qin, Chengjin Yu, Honggan Huang, Yixiang Liu, Chengliang Shanghai Jiao Tong Univ State Key Lab Mech Syst & Vibrat Shanghai Peoples R China

ISBN: (纸本)9781728156750

Recently, an increasing popularity of data-driven deep learning research in the field of machine fault diagnosis has been observed. Stacked denoising autoencoder (SDA), as a classic type of deep learning method, has been successfully used to learn effective representations for machine fault diagnosis. However, those previous studies always encounter with the inherent limitations of SDA: high computational cost, time-consuming training, and lack of scalability to high-dimensional data. Unfortunately, those limitations can restrict the applicability of those studies in real-world applications, which require timely model upgrade and fast real-time diagnosis. Besides, most previous studies concentrate on the vibration signal, and thus lack the attention towards other kinds of sensor data like acoustical signal. Therefore, to address the two problems above, inspired by the marginalized Stacked denoising autoencoder (mSDA), we adopt a variant of SDA for fast fault diagnosis on sound signal. In this way, the required stochastic gradient descent based on back propagation in traditional deep learning methods is replaced by a forward closed-form solution. Opposite to the time-consuming works which demand training thousands of parameters during optimization, this deep architecture only needs to determine a few hyper parameters in advance. To verify the effectiveness and efficiency of the proposal on sound signal, extensive empirical evaluation on a publicly available sound signal dataset of gear fault is carried on. Thorough comparisons with some state-of-the-art faulty diagnosis approaches, confirm the superiority of the proposal in high diagnostic accuracy and lower computational cost.

关键词： Machine fault diagnosis denoising autoencoder Acoustical Signal

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Bengali Handwritten Numeric Character Recognition using denoising autoencoders

Bengali Handwritten Numeric Character Recognition using Deno...

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IEEE International Conference on Engineering and Technology (ICETECH)

作者： Pal, Arghya Goa Univ Dept CST Taleigao Plateau 403206 Goa India

ISBN: (纸本)9781479918546

This work describes the recognition of Bengali Handwritten Numeral Recognition using Deep denoising autoencoder using Multilayer Perceptron (MLP) trained through backpropagation algorithm (DDA). To bring the weights of the DDA to some good solution a layer wise pre-training is done with denoising autoencoders. denoising autoencoders using MLP trained through backpropagation algorithm are made by introducing masking noise at input to the autoencoder to capture meaningful information while hidden layers are remain untouched at pre-training. Those pre-trained denoising autoencoders are then stacked to build a DDA. DDA is then converted to a Deep Classifier (DC) by using a final output layer. After a final fine-tune best DC is selected to discriminate classes. Performance of the DC using DDA is compared with the Deep autoencoder using MLP trained through backpropagation (DA) and Support Vector Machines (SVM). From the experiment it is evident that recognition performance of DDA that is 98.9% is higher than DA and SVM those are 97.3% and 97%. Using their performance at validation set results are further combined to build a Hybrid Recognizer that gives a performance of 99.1%

关键词： denoising autoencoder MLP Deep Network Handwriting Numeral Recognition

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Medical image denoising using convolutional denoising autoencoders 16

Medical image denoising using convolutional denoising autoen...

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16th IEEE International Conference on Data Mining (ICDM)

作者： Gondara, Lovedeep Simon Fraser Univ Dept Comp Sci Burnaby BC Canada

ISBN: (纸本)9781509059102

Image denoising is an important pre-processing step in medical image analysis. Different algorithms have been proposed in past three decades with varying denoising performances. More recently, having outperformed all conventional methods, deep learning based models have shown a great promise. These methods are however limited for requirement of large training sample size and high computational costs. In this paper we show that using small sample size, denoising autoencoders constructed using convolutional layers can be used for efficient denoising of medical images. Heterogeneous images can be combined to boost sample size for increased denoising performance. Simplest of networks can reconstruct images with corruption levels so high that noise and signal are not differentiable to human eye.

关键词： Image denoising denoising autoencoder convolutional autoencoder

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Recognition of online Handwritten Bangla Characters using Hierarchical System with denoising autoencoders 4

Recognition of online Handwritten Bangla Characters using Hi...

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INTERNATIONAL CONFERENCE ON COMPUTATION OF POWER, ENERGY, INFORMATION AND COMMUNICATION

作者： Pal, Arghya Pawar, J. D. Goa Univ Dept CST Taleigao 403206 Goa India

ISBN: (纸本)9781467365246

This work describes the recognition of online handwritten Bengali characters using Deep denoising autoencoder with Multilayer Perceptron (MLP) trained through backpropagation algorithm [I]. Initial pre-training has been done to the denoising autoencoder with MLP trained through backpropagation algorithm, to bring the weights of the Deep network to some good solution and then pre-trained denoising autoencoders are stacked to form a Deep denoising autoencoder (DDA). A final classification layer makes DDA to a Deep Classifier (DC) followed by a final fine-tune that gives the best classifier for the job of classification of Bengali characters. The overall system is hierarchical in nature and the system has been trained in two phase where the first phase has trained a broad classifier and in the second phase class specific recognizer has been trained. At the testing phase in this hierarchical approach, first a broad classifier has been used to recognize broad classes like Vowel, Consonant, Special Symbol and Numeral for a novel test sample. Once the broad class gets recognized then a class specific recognizer has been used to recognize the exact character the test sample belongs. Recognition performance of the hierarchical system is 93.12%.

关键词： MLP denoising autoencoder Deep Network Bengali Character Classification

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