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检索条件"主题词=encoder-decoder"

共 894 条记录，以下是121-130 订阅

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End-to-End Trained CNN encoder-decoder Networks for Image Steganography 15th

End-to-End Trained CNN Encoder-Decoder Networks for Image St...

引用

15th European Conference on Computer Vision (ECCV)

作者： Rehman, Atique ur Rahim, Rafia Nadeem, Shahroz ul Hussain, Sibt Natl Univ Comp & Emerging Sci NUCES FAST Reveal Recognit Vis & Learning Lab Islamabad Pakistan

ISBN: (纸本)9783030110185;9783030110178

All the existing image steganography methods use manually crafted features to hide binary payloads into cover images. This leads to small payload capacity and image distortion. Here we propose a convolutional neural network based encoder-decoder architecture for embedding of images as payload. To this end, we make following three major contributions: (i) we propose a deep learning based generic encoder-decoder architecture for image steganography;(ii) we introduce a new loss function that ensures joint end-to-end training of encoder-decoder networks;(iii) we perform extensive empirical evaluation of proposed architecture on a range of challenging publicly available datasets (MNIST, CIFAR10, PASCAL-VOC12, ImageNet, LFW) and report state-of-the-art payload capacity at high PSNR and SSIM values.

关键词： Steganography CNN encoder-decoder Deep neural networks

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Multi-step-ahead Host Load Prediction with GRU Based encoder-decoder in Cloud Computing 10

Multi-step-ahead Host Load Prediction with GRU Based Encoder...

引用

10th International Conference on Knowledge and Smart Technology (KST) - Cybernetics in the Next Decades

作者： Peng, Chenglei Li, Yang Yu, Yao Zhou, Yu Du, Sidan Nanjing Univ Sch Elect Sci & Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538640159

The details of the host workloads in cloud computing environment and the application demands of the real world computing system are becoming so complex that it throws a big challenge to the major cloud infrastructure vendors. To achieve service level agreements between users and cloud service vendors, it is essential to apply accurate prediction of future host load, which is also significant to improve the resource allocation and utilization in cloud computing. Although that there were several various methods and models developed, few of them can acquire the long-term temporal dependencies appropriately to make accurate predictions. In this paper, we apply a GRU based encoder-decoder network(GRUED) which contains two gated recurrent neural networks(GRUs) to address these issues. Thorough empirical studies based upon the Google resources usage traces and the traditional Unix system load traces demonstrate that our proposed method outperforms other state-of-the-art approaches for the prediction of multi-step-ahead host workload in cloud computing.

关键词： Cloud computing encoder-decoder Recurrent neural network Host load prediction GRU

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A Non-negative Symmetric encoder-decoder Approach for Community Detection 17

A Non-negative Symmetric Encoder-Decoder Approach for Commun...

引用

ACM Conference on Information and Knowledge Management (CIKM)

作者： Sun, Bing-Jie Shen, Huawei Gao, Jinhua Ouyang, Wentao Cheng, Xueqi Chinese Acad Sci Inst Comp Technol CAS Key Lab Network Data Sci & Technol Beijing Peoples R China

ISBN: (纸本)9781450349185

Community detection or graph clustering is crucial to understanding the structure of complex networks and extracting relevant knowledge from networked data. Latent factor model, e.g., non-negative matrix factorization and mixed membership block model, is one of the most successful methods for community detection. Latent factor models for community detection aim to find a distributed and generally low-dimensional representation, or coding, that captures the structural regularity of network and reflects the community membership of nodes. Existing latent factor models are mainly based on reconstructing a network from the representation of its nodes, namely network decoder, while constraining the representation to have certain desirable properties. These methods, however, lack an encoder that transforms nodes into their representation. Consequently, they fail to give a clear explanation about the meaning of a community and suffer from undesired computational problems. In this paper, we propose a non-negative symmetric encoder-decoder approach for community detection. By explicitly integrating a decoder and an encoder into a unified loss function, the proposed approach achieves better performance over state-of-the-art latent factor models for community detection task. Moreover, different from existing methods that explicitly impose the sparsity constraint on the representation of nodes, the proposed approach implicitly achieves the sparsity of node representation through its symmetric and non-negative properties, making the optimization much easier than competing methods based on sparse matrix factorization.

关键词： Community detection Latent factor model encoder-decoder

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A novel encoder-decoder structure for Time Series analysis based on Bayesian Uncertainty reduction

A novel Encoder-Decoder structure for Time Series analysis b...

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IEEE Latin American Conference on Computational Intelligence (LA-CCI)

作者： Llugsi, Ricardo El Yacoubi, Samira Fontaine, Allyx Lupera, Pablo Natl Polytech Sch Dept Elect Telecommun & Informat Networks DETRI Quito Ecuador Univ Perpignan Via Domitia UMR Espace Dev Perpignan France Univ Guyane UMR Espace Dev Cayenne French Guiana

ISBN: (纸本)9781728188645

In the present work, a novel Convolutional LSTM encoder-decoder structure for the implementation of Weather Forecast for the Andean city of Quito is presented. Aside from the above, the encoder-decoder structure uses a Walk-Forward validation, an adjustment of the Bayesian posterior predictive distribution and the ADAMW optimizer to carry out the forecast. The aforementioned stages are combined to obtain 4 error metrics per hour. The prediction is done in base of acquired data from a network of Automatic Weather Stations. The results show that the Convolutional encoder-decoder structure with a dropout probability of 0.05 and a model precision equal to 0.1 performs better than a LSTM model, LSTM Stacked model or ARIMA models reaching a maximum error of 1.03 degrees C. Finally, the methodology could be applied as an effective option to implement the post-processing stage for the physical model of a Weather Forecast System.

关键词： LSTM Time Series Convolutional encoder-decoder Neural Network Walk-Forward validation Dropout Bayesian uncertainty

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A Recurrent encoder-decoder Network for Sequential Face Alignment 14th

A Recurrent Encoder-Decoder Network for Sequential Face Alig...

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14th European Conference on Computer Vision (ECCV)

作者： Peng, Xi Feris, Rogerio S. Wang, Xiaoyu Metaxas, Dimitris N. Rutgers State Univ Piscataway NJ 08854 USA IBM TJ Watson Res Ctr Yorktown Hts NY USA Snapchat Res Venice CA USA

ISBN: (纸本)9783319464480;9783319464473

We propose a novel recurrent encoder-decoder network model for real-time video-based face alignment. Our proposed model predicts 2D facial point maps regularized by a regression loss, while uniquely exploiting recurrent learning at both spatial and temporal dimensions. At the spatial level, we add a feedback loop connection between the combined output response map and the input, in order to enable iterative coarse-to-fine face alignment using a single network model. At the temporal level, we first decouple the features in the bottleneck of the network into temporal-variant factors, such as pose and expression, and temporal-invariant factors, such as identity information. Temporal recurrent learning is then applied to the decoupled temporal-variant features, yielding better generalization and significantly more accurate results at test time. We perform a comprehensive experimental analysis, showing the importance of each component of our proposed model, as well as superior results over the state-of-the-art in standard datasets.

关键词： Recurrent learning encoder-decoder Face alignment

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T2S: An encoder-decoder Model for Topic-Based Natural Language Generation 23rd

T2S: An Encoder-Decoder Model for Topic-Based Natural Langua...

引用

23rd International Conference on Applications of Natural Language to Information Systems (NLDB)

作者： Ou, Wenjie Chen, Chaotao Ren, Jiangtao Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Guangdong Peoples R China

ISBN: (纸本)9783319919478;9783319919461

Natural language generation (NLG) plays a critical role in various natural language processing (NLP) applications. And the topics provide a powerful tool to understand the natural language. We propose a novel topic-based NLG model which can generate topic coherent sentences given single topic or combination of topics. The model is an extension of the recurrent encoder-decoder framework by introducing a global topic embedding matrix. Experimental results show that our encoder can not only transform a source sentence to a representative topic distribution which can give a better interpretation of the source sentence, but also generate topic coherent and diversified sentences given different topic distribution without any text-level input.

关键词： Natural language generation Topic encoder-decoder

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A COMPARISON OF TECHNIQUES FOR LANGUAGE MODEL INTEGRATION IN encoder-decoder SPEECH RECOGNITION

A COMPARISON OF TECHNIQUES FOR LANGUAGE MODEL INTEGRATION IN...

引用

IEEE Workshop on Spoken Language Technology (SLT)

作者： Toshniwal, Shubham Kannan, Anjuli Chiu, Chung-Cheng Wu, Yonghui Sainath, Tara N. Livescu, Karen Toyota Technol Inst Chicago IL 60637 USA Google Inc Mountain View CA USA

ISBN: (纸本)9781538643341

Attention-based recurrent neural encoder-decoder models present an elegant solution to the automatic speech recognition problem. This approach folds the acoustic model, pronunciation model, and language model into a single network and requires only a parallel corpus of speech and text for training. However, unlike in conventional approaches that combine separate acoustic and language models, it is not clear how to use additional (unpaired) text. While there has been previous work on methods addressing this problem, a thorough comparison among methods is still lacking. In this paper, we compare a suite of past methods and some of our own proposed methods for using unpaired text data to improve encoder-decoder models. For evaluation, we use the medium-sized Switchboard data set and the large-scale Google voice search and dictation data sets. Our results confirm the benefits of using unpaired text across a range of methods and data sets. Surprisingly, for first-pass decoding, the rather simple approach of shallow fusion performs best across data sets. However, for Google data sets we find that cold fusion has a lower oracle error rate and outperforms other approaches after second-pass rescoring on the Google voice search data set.

关键词： speech recognition encoder-decoder language model shallow fusion cold fusion deep fusion

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Dynamic-attention based encoder-decoder model for Speaker Extraction with Anchor speech

Dynamic-attention based Encoder-decoder model for Speaker Ex...

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Annual Summit and Conference of the Asia-Pacific-Signal-and-Information-Processing-Association (APSIPA ASC)

作者： Li, Hao Zhang, Xueliang Gao, Guanglai Inner Mongolia Univ Coll Comp Sci Hohhot Inner Mongolia Peoples R China

ISBN: (纸本)9781728132488

Speech plays an important role in human-computer interaction. For many real applications, an annoying problem is that speech is often degraded by interfering noise. Extracting target speech from background interference is a meaningful and challenging task, especially when interference is also human voice. This work addresses the problem of extracting target speaker from interfering speaker with a short piece of anchor speech which is used to obtain the target speaker identify. We propose a encoder-decoder neural network architecture. Specifically, the encoder transforms the anchor speech to a embedding which is used to represent the identity of target speaker. The decoder utilizes the speaker identity to extract the target speech from mixture. To make a acoustic-related speaker identity, The dynamic-attention mechanism is utilized to build a time-varying embedding for each frame of the mixture. Systematic evaluation indicates that our approach improves the quality of speaker extraction.

关键词： speaker extraction dynamic-attention encoder-decoder

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An encoder-decoder Approach to Handwritten Mathematical Expression Recognition with Multi-head Attention and Stacked decoder 16th

An Encoder-Decoder Approach to Handwritten Mathematical Expr...

引用

16th IAPR International Conference on Document Analysis and Recognition (ICDAR)

作者： Ding, Haisong Chen, Kai Huo, Qiang Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9783030863319

encoder-decoder framework with attention mechanism has become a mainstream solution to handwritten mathematical expression recognition (HMER) since "watch, attend and parse (WAP)" approach was proposed in 2017, where a convolutional neural network is used as encoder and a gated recurrent unit with attention is used in decoder. Inspired by the recent success of Transformer in many applications, in this paper, we adopt the design of multi-head attention and stacked decoder in Transformer to improve the decoder part of the WAP framework for HMER. Experimental results on CROHME tasks show that multi-head attention can boost the expression recognition rate (ExpRate) of WAP from 54.32%/58.05% to 56.76%/59.72% and stacked decoder can further improve ExpRate to 57.72%/61.38% on CROHME 2016/2019 test sets.

关键词： encoder-decoder Multi-head attention Stacked decoder Transformer Mathematical expression recognition

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Table Structure Recognition Using CoDec encoder-decoder 16th

Table Structure Recognition Using CoDec Encoder-Decoder

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16th IAPR International Conference on Document Analysis and Recognition (ICDAR)

作者： Pegu, Bhanupriya Singh, Maneet Agarwal, Aakash Mitra, Aniruddha Singh, Karamjit Mastercard AI Garage Gurgaon India

ISBN: (纸本)9783030861599;9783030861582

Automated document analysis and parsing has been the focus of research since a long time. An important component of document parsing revolves around understanding tabular regions with respect to their structure identification, followed by precise information extraction. While substantial effort has gone into table detection and information extraction from documents, table structure recognition remains to be a long-standing task demanding dedicated attention. The identification of the table structure enables extraction of structured information from tabular regions which can then be utilized for further applications. To this effect, this research proposes a novel table structure recognition pipeline consisting of row identification and column identification modules. The column identification module utilizes a novel Column Detector encoder-decoder model (termed as CoDec encoder decoder) which is trained via a novel loss function for predicting the column mask for a given input image. Experiments have been performed to analyze the different components of the proposed pipeline, thus supporting their inclusion for enhanced performance. The proposed pipeline has been evaluated on the challenging ICDAR 2013 table structure recognition dataset, where it demonstrates state-of-the-art performance.

关键词： Table structure recognition encoder-decoder Document analysis

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