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检索条件"主题词=Auto-Encoder"

共 783 条记录，以下是131-140 订阅

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Jointly Sparse Support Recovery via Deep auto-encoder with Applications in MIMO-based Grant-Free Random Access for mMTC 21

Jointly Sparse Support Recovery via Deep Auto-encoder with A...

引用

21st IEEE International Workshop on Signal Processing Advances in Wireless Communications (IEEE SPAWC)

作者： Zhang, Wanqing Li, Shuaichao Cui, Ying Shanghai Jiao Tong Univ Dept Elect Engn Shanghai Peoples R China

ISBN: (纸本)9781728154787

In this paper, a data-driven approach is proposed to jointly design the common sensing (measurement) matrix and jointly support recovery method for complex signals, using a standard deep auto-encoder for real numbers. The auto-encoder in the proposed approach includes an encoder that mimics the noisy linear measurement process for jointly sparse signals with a common sensing matrix, and a decoder that approximately performs jointly sparse support recovery based on the empirical covariance matrix of noisy linear measurements. The proposed approach can effectively utilize the feature of common support and properties of sparsity patterns to achieve high recovery accuracy, and has significantly shorter computation time than existing methods. We also study an application example, i.e., device activity detection in Multiple-Input Multiple-Output (MIMO)-based grant-free random access for massive machine type communications (mMTC). The numerical results show that the proposed approach can provide pilot sequences and device activity detection with better detection accuracy and substantially shorter computation time than well-known recovery methods.

关键词： Jointly sparse support recovery deep learning auto-encoder activity detection grant-free random access

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Situational Estimation of Sports Broadcasting using a Character Level auto-encoder for Live Tweets

Situational Estimation of Sports Broadcasting using a Charac...

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IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)

作者： Fujimoto, Nodoka Ushiama, Taketoshi Kyushu Univ Grad Sch Design Fukuoka Japan Kyushu Univ Fac Design Fukuoka Japan

ISBN: (纸本)9781665419246

Real-time videos enable people to experience real-world events in real time. However, it is challenging for people to keep watching an event when they are busy and have other activities to carry out. It is therefore crucial to develop a method for supporting the viewers' real-time video viewing effectively. Using sports broadcasting as a representative example of such videos, our goal is to develop a system that detects and automatically estimates the events in sports games that are likely to be of high value to the viewers and notifies them in real time during sports broadcasts. To this end, we propose a general-purpose method that efficiently models situations from small amounts of tweets without using domain-specific knowledge. The effectiveness of the method was verified via experiments by estimating the number of tweets posted from the modeled features of a situation with high accuracy.

关键词： SNS Live Tweet auto-encoder Deep Learning

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Knowledge-Guided auto-encoder for Unsupervised Angle-of-Arrival Estimation 25

Knowledge-Guided Auto-Encoder for Unsupervised Angle-of-Arri...

引用

IEEE Wireless Communications and Networking Conference (IEEE WCNC)

作者： Guo, Zhiheng Xiao, Yuanzhang Wang, Xijun Chen, Xiang Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou Peoples R China Univ Hawaii Hawaii Adv Wireless Technol Inst Honolulu HI USA

ISBN: (纸本)9798350303582;9798350303599

In this paper, we propose a highly accurate unsupervised deep learning framework based on auto-encoder (AE) for angle-of-arrival (AoA) estimation in massive MIMO systems. Our method builds on an improvement of the vanilla AE by incorporating the knowledge of signal propagation models into the decoder. In our proposed knowledge-guided AE (KG-AE), instead of having learnable parameters, the decoder has fixed weights that implement the signal propagation model. Such modification forces the encoder to output meaningful physical parameters of interest (i.e., AoA), which cannot be achieved by standard AE. Furthermore, we rigorously analyze the multiplicity of local optima in unsupervised channel estimation problems. Our analysis informs the design of the cost function and the training scheme for the proposed KG-AE. Specifically, we design a two-stage training scheme, different loss functions in the two stages to achieve good initial points and boost the performance of the proposed KG-AE, respectively. Finally, extensive simulations are performed, and the results corroborate the analysis and demonstrate the performance improvements of the proposed KG-AE over the subspace-based algorithms and the state-of-the-art unsupervised learning-based algorithm.

关键词： Angle-of-arrival Estimation auto-encoder Unsupervised Learning

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EXPLORING THE RELATIONSHIPS BETWEEN SCATTERING PHYSICS AND auto-encoder LATENT-SPACE EMBEDDING

EXPLORING THE RELATIONSHIPS BETWEEN SCATTERING PHYSICS AND A...

引用

IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： De, Shaunak Clanton, Christian Bickerton, Steven Baney, Oliwia Patnaik, Kaushik Orbital Insight Inc 3000 El Camino Real4th Floor Palo Alto CA 94303 USA

ISBN: (纸本)9781728163741

Polarimetric SAR (PolSAR) is uniquely able to capture structural and compositional properties of targets leading to improved performance in various classification applications over traditional single-polarization SAR. To aid in the interpretation of the return scatter, several decomposition techniques have been developed that attempt to classify the scene by presenting the return power as a combination of pre-determined canonical targets. For most decomposition techniques, these relationships are derived from the physics of radar scatter, and thus the results of the segmentation are explainable in terms of observable physical phenomena. Recently, Deep Neural Networks (DNNs) have emerged as a leading strategy for classifying PolSAR data. While effective, these techniques are dependent on discovering relationships between the data and provided supervised labels during an exploratory phase of the algorithm called "training" The inter-dependencies are embedded into the weights of the network and are subsequently used to perform classification of the unlabeled data samples. Since the entire process is data-driven, it can be difficult to explain the outcomes of the algorithm physically. In this paper, we begin to explore the relationship between radar physics and the latent space embedded in the networks during supervised training. The goal is to explore current and future possibilities of explaining the results of deep neural networks and relating their outputs to radar physics. This can help improve confidence in the outputs of DNNs and potentially illuminate strategies to further improve their performance by embedding physical constraints in their training and classification.

关键词： Polarimetry Synthetic Aperture Radar auto-encoder Neural Networks

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Schizophrenia Detection Based on EEG Using Recurrent auto-encoder Framework 29th

Schizophrenia Detection Based on EEG Using Recurrent Auto-en...

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29th International Conference on Neural Information Processing

作者： Wu, Yihan Xia, Min Wang, Xiuzhu Zhang, Yangsong Southwest Univ Sci & Technol Sch Comp Sci & Technol Lab Brain Sci & Med Artificial Intelligence Mianyang 621010 Sichuan Peoples R China Univ Elect Sci & Technol China MOE Key Lab Neuroinformat Chengdu 610054 Peoples R China

ISBN: (纸本)9783031301070;9783031301087

Schizophrenia (SZ) is a serious mental disorder that could seriously affect the patient's quality of life. In recent years, detection of SZ based on deep learning (DL) using electroencephalogram (EEG) has received increasing attention. In this paper, we proposed an end-to-end recurrent auto-encoder (RAE) model to detect SZ. In the RAE model, the raw data was input into one auto-encoder block, and the reconstructed data were recurrently input into the same block. The extracted code by auto-encoder block was simultaneously served as an input of a classifier block to discriminate SZ patients from healthy controls (HC). Evaluated on the dataset containing 14 SZ patients and 14 HC subjects, and the proposed method achieved an average classification accuracy of 81.81% in subject-independent experiment scenario. This study demonstrated that the structure of RAE is able to capture the differential features between SZ patients and HC subjects.

关键词： EEG Schizophrenia detection auto-encoder Convolutional neural network

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Multiple-Input auto-encoder for IoT Intrusion Detection Systems with Heterogeneous Data 59

Multiple-Input Auto-Encoder for IoT Intrusion Detection Syst...

引用

59th Annual IEEE International Conference on Communications (IEEE ICC)

作者： Phai Vu Dinh Dinh Thai Hoang Nguyen Quang Uy Nguyen, Diep N. Son Pham Bao Dutkiewicz, Eryk Univ Technol Sydney Sch Elect & Data Engn Sydney NSW Australia Vietnam Natl Univ VNU Univ Engn & Technol JTIRC Hanoi Vietnam Le Quy Don Tech Univ Hanoi Vietnam

ISBN: (纸本)9781728190549

Machine learning is a core component of many Intrusion Detection Systems (IDS) for IoT networks. However, developing a robust machine-learning model for IDSs on IoT networks is very challenging. This is due to the diversity of IoT devices resulting in the inconsistency and high dimensions of data collected from IoT networks. In other words, in IoT environments, the training data for IDSs on IoT networks is often heterogeneous since they are collected from multiple sources with different characteristics. To tackle these problems, this paper proposes a novel neural network architecture called Multiple-Input auto-encoder (MIAE). MIAE has multiple sub-encoders that can process multiple input sources with different dimensions. Moreover, the MIAE model is trained in an unsupervised learning mode, and it can transfer the heterogeneous inputs into lower-dimensional representation to facilitate classifiers to distinguish between the normal samples and types of attacks. The experimental results on the three most popular benchmark IDS datasets, i.e., NSLKDD, UNSW-NB15, and IDS2017, show the superior performance of the MIAE over three groups of methods including conventional classifiers, state-of-the-art dimensionality reduction models, and unsupervised representation learning methods for multiple inputs with different dimensions. MIAE combined with the Random Forest (RF) classifier also achieves 96.2% in terms of accuracy in detecting sophisticated attacks, e.g., Slowloris. In addition, the average running time for detecting an attack sample obtained by MIAE combined with RF classifier is only roughly 5E-7 seconds, whilst the model size is lower than 1 MB. This clearly shows the effectiveness of our proposed model when deployed in practice.

关键词： auto-encoder dimensionality reduction heterogeneous data intrusion detection IoT

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Morphological, Texture and auto-encoder based Feature Extraction Techniques for Skin Disease Classification 16

Morphological, Texture and Auto-encoder based Feature Extrac...

引用

16th IEEE-India-Council International Conference (INDICON)

作者： Chatterjee, S. Dey, D. Munshi, S. Jadavpur Univ Dept Elect Engn Kolkata 700032 India

ISBN: (数字)9781728123271

ISBN: (纸本)9781728123271

Development of computer aided diagnostic system for skin disease identification has a great impact on early and accurate diagnosis of the ailment. It is a challenging task for the experts to make diagnosis only by visual inspection of the skin lesion area or of the related dermoscopic images, owing to the closely similar appearance and complex structural property. Here, the morphological and wavelet based fractal texture features have been used along with the stacked auto-encoder based features for the identification of four disease classes from dermoscopic images. The introduction of auto-encoder based features in combination with handcrafted features has classified the melanoma, nevus, basal cell carcinoma (BCC) and seborrheic keratosis (SK) diseases with the correct identification accuracy of 96.71%, 97.77%, 98.03% and 98.11% respectively.

关键词： auto-encoder dermoscopic image classification wavelet fractal

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Application of auto-encoder and Attention Mechanism in Raman Spectroscopy 18th

Application of Auto-encoder and Attention Mechanism in Raman...

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18th International Conference on Intelligent Computing (ICIC)

作者： Bai, Yunyi Xu, Mang Qian, Pengjiang Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Jiangsu Peoples R China

ISBN: (纸本)9783031138324;9783031138317

Conventional Raman spectroscopy, which is based on the qualitative or quantitative determination of substances, has been widely utilized in industrial manufacture and academic research. However, in traditional Raman spectroscopy, human experience plays a prominent role. Because of the massive amount of comparable information contained in the spectrograms of varying concentration media, the extraction of feature peaks is especially crucial. Although manual feature peak extraction in spectrograms might reduce signal dimensionality to a certain extent, it could also result in spectral information loss, misclassification, and underclassification of feature peaks. This research solves the problem by extracting a feature dimensionality reduction method based on an auto-encoder-attention mechanism, applying a deep learning approach to spectrogram feature extraction, and feeding the features into a neural network for concentration prediction. After rigorous testing, the model's prediction accuracy may reach a unit concentration of 0.01 with a 13% error, providing a reliable aid to manual and timely culture medium replenishment. And through extensive comparison experiments, it is concluded that the self-encoder-based dimensionality reduction method is more accurate compared with the machine learning method. The research demonstrates that using Raman spectroscopy to deep learning can produce positive outcomes and has great potential.

关键词： Raman spectroscopy Deep learning auto-encoder Attentional mechanisms

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Variational auto-encoder Based Variability Encoding for Dysarthric Speech Recognition 22

Variational Auto-Encoder Based Variability Encoding for Dysa...

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Interspeech Conference

作者： Xie, Xurong Ruzi, Rukiye Liu, Xunying Wang, Lan Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781713836902

Dysarthric speech recognition is a challenging task due to acoustic variability and limited amount of available data. Diverse conditions of dysarthric speakers account for the acoustic variability, which make the variability difficult to be modeled precisely. This paper presents a variational auto-encoder based variability encoder (VAEVE) to explicitly encode such variability for dysarthric speech. The VAEVE makes use of both phoneme information and low-dimensional latent variable to reconstruct the input acoustic features, thereby the latent variable is forced to encode the phoneme-independent variability. Stochastic gradient variational Bayes algorithm is applied to model the distribution for generating variability encodings, which are further used as auxiliary features for DNN acoustic modeling. Experiment results conducted on the UASpeech corpus show that the VAEVE based variability encodings have complementary effect to the learning hidden unit contributions (LHUC) speaker adaptation. The systems using variability encodings consistently outperform the comparable baseline systems without using them, and obtain absolute word error rate (WER) reduction by up to 2.2% on dysarthric speech with "Very low" intelligibility level, and up to 2% on the "Mixed" type of dysarthric speech with diverse or uncertain conditions.

关键词： dysarthric speech recognition acoustic variability variational auto-encoder

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CONTRASTIVE auto-encoder FOR PHONEME RECOGNITION

CONTRASTIVE AUTO-ENCODER FOR PHONEME RECOGNITION

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Zheng, Xin Wu, Zhiyong Meng, Helen Cai, Lianhong Tsinghua Univ Grad Sch Shenzhen Tsinghua CUHK Joint Res Ctr Media Sci Technol & S Shenzhen 518055 Peoples R China

ISBN: (纸本)9781479928934

Speech data typically contains task irrelevant information lying within features. Specifically, phonetic information, speaker characteristic information, emotional information and noise are always mixed together and tend to impair one another for certain task. We propose a new type of auto-encoder for feature learning called contrastive auto-encoder. Unlike other variants of auto-encoders, contrastive auto-encoder is able to leverage class labels in constructing its representation layer. We achieve this by modeling two auto-encoders together and making their differences contribute to the total loss function. The transformation built with contrastive auto-encoder can be seen as a task-specific and invariant feature learner. Our experiments on TIMIT clearly show the superiority of the feature extracted from contrastive auto-encoder over original acoustic feature, feature extracted from deep auto-encoder, and feature extracted from a model that contrastive auto-encoder originates from.

关键词： auto-encoder contrastive auto-encoder phoneme recognition deep neural network (DNN) restricted Boltzmann machine (RBM)

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