检索结果-内蒙古大学图书馆

6th International Conference on Inventive Computation Technologies (ICICT)

作者： Saradagi, Ankit B. Jeyakumar, G. Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Comp Sci & Engn Coimbatore Tamil Nadu India

ISBN: (纸本)9781728185019

The work presented in this paper focuses on the use of Evolutionary Algorithms (EA) in two different fields of interest where researchers are keenly working on - encoder decoder and Generative Adversarial Network architectures. These architectures have brought in wonders to the field of Image processing. They gain interests of researches in various other fields like image domain mappings, interactive generation, security, detection of patterns and processing etc. The key aspects that determine a good performance of a system are its model architecture and optimal hyper parameters that drives it. This paper proposes two new analysis with Evolutionary Algorithms 1) Hyper parameter optimization of encoder decoder Model for the task of Image Inpainting and 2) Addressing the input noise vector of the Generative Adversarial Network model. In the first analysis, the research optimizes the task of semantic inpainting. The aim of inpainting is to do the semantic filling of the missing regions in original image. Inpainting task is used across various domains like medical imaging to reconstruct the damaged body parts and tissues, satellite imaging and surveillance to have semantic filling of images taken in uncertain weather conditions and in photography which allows the range of fillings possible to better fulfill the customer needs etc. The research found a set of hyper parameters which can perform similar to/outperform the manually hard coded parameters. It is really difficult task for a person to try out all the combinations and evolutionary algorithms tend to do it very easily. In second analysis, the research focuses on the input noise vector to the generator of Generative Adversarial Network. After training the generator is capable of mapping the random noise vector to an image. This study addresses grouping all those vectors together which are capable of mapping to a particular class of generation using evolutionary algorithms. This way the present study addressed the p

关键词： Evolutionary Algorithms Genetic Algorithm Differential Evolution encoder decoder Generative Adversarial Network Inpainting Noise Vector Binary Cross-Entropy Dice Coefficient Mean Absolute Error

来源：评论

学校读者我要写书评

暂无评论

Recognizing Unconstrained Vietnamese Handwriting By Attention Based encoder decoder Model

Recognizing Unconstrained Vietnamese Handwriting By Attentio...

引用

International Conference on Advanced Computing and Applications (ACOMP)

作者： Anh Duc Le Hung Tuan Nguyen Nakagawa, Masaki Ctr Open Data Humanities Tokyo Japan Tokyo Univ Agr & Technol Dept Comp & Informat Sci Tokyo Japan

ISBN: (纸本)9781538691861

Inspired by recent successes in neural machine translation and image caption generation, we present an attention based encoder decoder model (AED) to recognize Vietnamese Handwritten Text. The model composes of three parts: a convolution neural network (CNN) for extracting invariant features, a Bidirectional Long Short-Term Memory network (BLSTM) for encoding extracted features (BLSTM encoder), and a Long Short-Term Memory network (LSTM) with an attention model incorporated for generating output text (LSTM decoder), which are connected from the CNN part to the BLSTM encoder and finally the LSTM decoder. The input of the CNN part is a handwritten text image and the target of the LSTM decoder is the corresponding text of the input image. Our model is trained end-to-end to predict the text from a given input image since all the parts are differential components. In the experiment section, we evaluate our proposed AED model on the VNOnDB-Word database to verify its efficiency. The experiential results show that our model achieves 12.30% of word error rate without using language model. This result is competitive with the handwriting recognition system provided by Google in the Vietnamese Online Handwritten Text Recognition competition.

关键词： Vietnamese online handwriting recognition Recognition of unconstrained Vietnamese handwriting encoder decoder attention model

来源：评论

学校读者我要写书评

暂无评论

A novel weber cross information sharing deep learning encoder decoder model for emotion recognition using facial expression 4

A novel weber cross information sharing deep learning encode...

引用

4th International Conference on Artificial Intelligence and Signal Processing

作者： Jeen, Retna Kumar R. Stanley, Berakhah. F. Gnanaprakash, V Bini, Palas P. Purusothaman, K. E. Joel, Devadass Daniel D. J. Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Elect & Commun Engn Chennai Tamil Nadu India SRM Easwari Engn Coll Elect & Commun Engn Chennai Tamil Nadu India

ISBN: (纸本)9798350350661;9798350350654

This study introduces an innovative deep learning framework, the Weber Cross Information Sharing Deep Learning encoder-decoder (WCISD-ED) model, designed for emotion recognition through facial expression analysis. Recognition of emotion is a pivotal aspect of man-machine interaction, offering profound implications in areas ranging from mental health assessment to customer service and entertainment. However, because human expressions are so subtle and varied, accurately deducing emotions from facial expressions is a sophisticated task. The WCISD-ED model is crafted to address these complexities by incorporating principles derived from Weber's Law, which relates to the perception of changes in visual stimuli. This integration enhances the model's sensitivity to the minute yet critical variations in facial expressions associated with different emotions. The model features a novel cross information sharing structure within an encoder-decoder architecture, enabling the effective processing of facial features at multiple scales and depths. The encoder segment of the model focuses on the detailed extraction of facial features, while the decoder reconstructs these features into recognizable emotion categories. The cross information sharing mechanism allows for the interaction between different layers of the network, facilitating a more comprehensive and nuanced understanding of facial expressions. Extensive testing on diverse datasets demonstrates that the WCISD-ED model significantly outperforms existing emotion recognition models in terms of accuracy and reliability.

关键词： Facial emotion weber cross information sharing encoder decoder face expression

来源：评论

学校读者我要写书评

暂无评论

Power optimization of a single-core processor using LSTM based encoder-decoder model for online DVFS

引用

SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2023年第2期48卷 1-15页

作者： Thethi, Sukhmani Kaur Kumar, Ravi Thapar Inst Engn & Technol Dept Elect & Commun Engn Patiala 147001 India

Due to the interaction and interdependency between various hardware/software units and policies of the processors with application running on it, real-time embedded systems are quite complex in nature. Furthermore, smart adaptability of supply clock and voltage is required in order to optimize power without compromising on the performance, depending upon the type of application running and tasks that the application involved. This is done using Dynamic Voltage and Frequency Scaling (DVFS) technique. A novel DVFS technique is proposed in this paper which models frequency scaling as a recurrent network problem. This approach has successfully been able to capture the intricate dependencies amongst various factors influencing the operation. We employed application independent- Radial Basis Neural Network to generate series of predicted frequencies for current workload of the processor, followed by seq2seq-LSTM based encoder decoder model to decide whether the frequency generated by the ANN model is optimum to conserve power of the embedded device or not. The proposed model predicts the workload and then compares the predicted frequency to the critical value or deadline of the current task pertaining to the application running. The experiments were conducted on a single core processor (RPi Zero) on which a benchmark application named "basicmath" from MiBench suite was run, and promising prediction accuracy rates were obtained without compromising on performance parameter's degradation.

关键词： Attention DNN DVFS encoder decoder LSTM power optimization

来源：评论

学校读者我要写书评

暂无评论

Category-Controlled encoder-decoder for Fake News Detection

引用

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第2期35卷 1242-1257页

作者： Wu, Lianwei Rao, Yuan Zhang, Cong Zhao, Yongqiang Nazir, Ambreen Xi An Jiao Tong Univ Sch Software Engn Xian Key Lab Social Intelligence & Complex Data Pr Xian 710054 Shaanxi Peoples R China Xi An Jiao Tong Univ Shaanxi Joint Key Lab Artifact Intelligence Sublab Xian 710054 Shaanxi Peoples R China Xian Huanyu Satellite Control & Data Applicat Co L Xian 710065 Shaanxi Peoples R China

The existing data-driven approaches typically capture credibility-indicative representations from relevant articles for fake news detection, such as skeptical and conflicting opinions. However, these methods still have several drawbacks: 1) Due to the difficulty of collecting fake news, the capacity of the existing datasets is relatively small;and 2) there is considerable unverified news that lacks conflicting voices in relevant articles, which makes it difficult for the existing methods to identify their credibility. Especially, the differences between true and fake news are not limited to whether there are conflict features in their relevant articles, but also include more extensive hidden differences at the linguistic level, such as the perspectives of emotional expression (like extreme emotion in fake news), writing style (like the shocking title in clickbait), etc., the existing methods are difficult to fully capture these differences. To capture more general and wide-ranging differences between true and fake news, in this paper, directly from the different categories of news itself, we propose a Category-controlled encoder-decoder model (CED) to generate examples with category-differentiated features and extend the dataset capacity to achieve data enhancement effect, thus enhancing fake news detection. Specifically, to make the generated examples enrich more news features, we develop news-guided encoder to guide relevant articles to generate news-semantic context representations. To drive the generated examples to contain more category-differentiated features, we devise category-controlled decoder which relies on pattern-shared unit to respectively capture intra-category shared features within true or fake news, and employs restriction unit to force the two types of shared features to be more different for highlighting inter-category differentiated features. The experimental results on three datasets demonstrate the superiority of CED.

关键词： Feature extraction Semantics Decoding Social networking (online) Task analysis Data models Recurrent neural networks encoder decoder fake news detection social media analysis natural language processing

来源：评论

学校读者我要写书评

暂无评论

DXNet: An encoder-decoder Architecture with XSPP for Semantic Image Segmentation in Street Scenes 1

引用

26th International Conference on Neural Information Processing (ICONIP) of the Asia-Pacific-Neural-Network-Society (APNNS)

作者： Shang, Yexin Zhong, Shan Gong, Shengrong Zhou, Lifan Ying, Wenhao Soochow Univ Sch Comp Sci & Technol Suzhou 215006 Peoples R China Changshu Inst Technol Sch Comp Sci & Engn Suzhou 215500 Peoples R China

ISBN: (数字)9783030368029

ISBN: (纸本)9783030368029;9783030368012

Semantic image segmentation plays a crucial role in scene understanding tasks. In autonomous driving, the driving of the vehicle causes the scale changes of objects in the street scene. Although multi-scale features can be learned through concatenating multiple different atrous-convolved features, it is difficult to accurately segment pedestrians with only partial feature information due to factors such as occlusion. Therefore, we propose a Xiphoid Spatial Pyramid Pooling method integrated with detailed information. This method, while connecting the features of multiple atrous-convolved, retains the image-level features of target boundary information. Based on the above methods, we design an encoder-decoder architecture called DXNet. The encoder is composed of a deep convolution neural network and two XSPP modules, and the decoder decodes the advanced features through up-sampling operation and skips connection to gradually restore the target boundary. We evaluate the effectiveness of our approach on the Cityscapes dataset. Experimental results show that our method performs better in the case of occlusion, and the mean intersection-over-union score of our model outperforms some representative works.

关键词： Semantic segmentation encoder decoder Spatial pyramid pooling Convolutional neural network

来源：评论

学校读者我要写书评

暂无评论

Crowd Density Forecasting with Distributed Camera-Based Nodes using encoder-decoder Long Short-Term Memory Network

Crowd Density Forecasting with Distributed Camera-Based Node...

引用

IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)

作者： Palo, Imran Jabian, Marven Aldueso, Karl Martin Mindanao State Univ Elect Engn Dept Iligan Inst Technol Iligan Philippines

ISBN: (纸本)9798350372113;9798350372106

Integration of multiple camera-based devices, devices designed for real-time crowd counting, is explored for crowd density forecasting in an area. Crowd density forecasting in such case, where future crowd counts in multiple time steps for each sensing region of individual cameras are being predicted, is a peculiar task. This paper employs encoder-decoder Long Short-Term Memory network to solve this problem. Overcounting may also exist in the presence of overlapping sensing regions of the cameras which is tackled in this work by correcting the crowd counts before forecasting through a neural network-based approach. Thus, this study proposes a two-stream network for crowd density forecasting. The network is trained and evaluated on data generated from an actual setup emulating the presumed output of multiple camera-based crowd-counting devices. Overall, the proposed framework delivers promising results based on the evaluation.

关键词： encoder decoder lstm crowd forecasting

来源：评论

学校读者我要写书评

暂无评论

Standard And Inception-Based encoder-decoder Neural Networks For Predicting The Solution Convergence Of Design Optimization Algorithms

Standard And Inception-Based Encoder-Decoder Neural Networks...

引用

作者： Nathanial James O'Neill University of Colorado

学位级别：硕士

The goal of this work is to investigate the ways in which the capabilities of machine learning algorithms, specifically those of neural networks, can be leveraged to enhance the performance of design optimization algorithms Â â specifically those of topology optimization. A recent boom of interest in design optimization has occurred, coinciding with the arrival and development of advanced manufacturing techniques (such as 3D printing and additive manufacturing) which are compatible with the designs generated by these algorithms. Neural networks have seen an even larger boom in interest and development for their ability to act as ``universal function generators;" in other words, for their ability to learn highly non-linear functions that approximate the behavior of extremely complex systems. Merging design optimization algorithms with the capabilities of neural networks poses several distinct possibilities: drastically reducing optimization time by predicting solution convergence; up-scaling solution resolution using Generative Adversarial Networks (GAN's); predicting solutions with no iteration; predicting and recognizing features in the optimized solution, just to name a few. In this thesis, three neural network architectures are tested for their ability to act as solution convergence predictors of a density-based topology optimization solver. The problem is posed as an image segmentation problem, and the neural networks are all trained on a 40,000 example training set with each example containing 100 iterations from the open source optimization solver Topy, (a data set created by Sosnovik et al (2017)). The third network developed and tested is a novel hybrid network Â â an inception encoder-decoder network Â â which is found to outperform the other networks on the prediction task at hand.

关键词： convolutional neural network design optimization encoder decoder inception neural network topology optimization Aerospace Engineering Computer Sciences Mechanical Engineering

来源：评论

学校读者我要写书评

暂无评论

Standard and Inception-Based encoder-decoder Neural Networks for Predicting the Solution Convergence of Design Optimization Algorithms

Standard and Inception-Based Encoder-Decoder Neural Networks...

引用

作者： O'Neill, Nathanial James University of Colorado Boulder

学位级别：硕士

The goal of this work is to investigate the ways in which the capabilities of machine learning algorithms, specifically those of neural networks, can be leveraged to enhance the performance of design optimization algorithms -- specifically those of topology optimization. A recent boom of interest in design optimization has occurred, coinciding with the arrival and development of advanced manufacturing techniques (such as 3D printing and additive manufacturing) which are compatible with the designs generated by these algorithms. Neural networks have seen an even larger boom in interest and development for their ability to act as ``universal function generators;" in other words, for their ability to learn highly non-linear functions that approximate the behavior of extremely complex systems. Merging design optimization algorithms with the capabilities of neural networks poses several distinct possibilities: drastically reducing optimization time by predicting solution convergence; up-scaling solution resolution using Generative Adversarial Networks (GAN's); predicting solutions with no iteration; predicting and recognizing features in the optimized solution, just to name a few. In this thesis, three neural network architectures are tested for their ability to act as solution convergence predictors of a density-based topology optimization solver. The problem is posed as an image segmentation problem, and the neural networks are all trained on a 40,000 example training set with each example containing 100 iterations from the open source optimization solver Topy, (a data set created by Sosnovik et al (2017)). The third network developed and tested is a novel hybrid network -- an inception encoder-decoder network -- which is found to outperform the other networks on the prediction task at hand.

关键词： design optimization convolutional neural network inception topology optimization encoder decoder neural network

来源：评论

学校读者我要写书评

暂无评论

Disguise Resilient Face Verification

引用

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2022年第6期32卷 3895-3905页

作者： Singh, Maneet Nagpal, Shruti Singh, Richa Vatsa, Mayank IIIT Delhi Dept Comp Sci & Engn New Delhi 110020 India IIT Jodhpur Dept Comp Sci & Engn Jodhpur 342037 Rajasthan India

With increasing usage of face recognition algorithms, it is well established that external artifacts and makeup accessories can be applied to different facial features such as eyes, nose, mouth, and cheek, to obfuscate one's identity or to impersonate someone else's identity. Recognizing faces in the presence of' these artifacts comprises the problem of' disguised face recognition, which is one of the most arduous covariates of face recognition. The challenge becomes exacerbated when disguised faces are captured in real-time environment, with low resolution images. To address the challenge of disguised face recognition, this paper first proposes a novel multi-objective encoder-decoder network, termed as DED-Net. DED-Net attempts to learn the class variations in the feature space generated by both disguised as well non-disguised images, using a combination of Mahalanobis and Cosine distance metrics, along with Mutual Information based supervision. The DED-Net is then extended to learn from the local and global features of both disguised and non-disguised face images for efficient face recognition, and the complete framework is termed as Disguise Resilient (D-Res) framework. The efficacy of the proposed framework has been demonstrated on two real-world benchmark datasets: Disguised Faces in the Wild (DFW) 2018 and DFW2019 competition datasets. In addition, this research also emphasizes on the importance of recognizing disguised faces in low resolution settings and proposes three experimental protocols to simulate the real-world surveillance scenario. To this effect, benchmark results have been shown on seven protocols for three low resolution settings (32 x 32, 24 x 24, and 16 x 16) of the two DFW benchmark datasets. The results demonstrate superior performance of the D-Res framework, in comparison with benchmark algorithms. For example, an improvement of around 3% is observed on the Overall protocol of the DFW2019 dataset, where the D-Res framework achieves 96.3%. Ex

关键词： Disguise face recognition encoder decoder low resolution

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：