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16th International Conference on Signal-Image Technology and Internet-Based Systems (SITIS)

作者： Su, Qiqi Iliadou, Eleftheria Univ London Dept Comp Sci City London England Natl & Kapodistrian Univ Dept Otorhinolaryngol 1 Athens Med Sch Athens Greece

ISBN: (纸本)9781665464956

Understanding the factors that contribute to optimal hearing aid fitting and hearing aid user experiences is crucial in order to increase the satisfaction and quality of life of hearing loss patients, as well as reduce societal and financial burdens. This work proposes a novel framework that uses encoder-decoder with attention mechanism ( attn-ED) for predicting future hearing aid usage and SHAP to explain the factors contributing to this prediction. It has been demonstrated in experiments that attn-ED performs well at predicting future hearing aid usage, and that SHAP can be utilized to calculate the contribution of different factors affecting hearing aid usage. This framework aims to establish confidence that AI models can be utilized in the medical domain with the use of XAI methods. Moreover, the proposed framework can also assist clinicians in determining the nature of interventions.

关键词： XAI Hearing Loss encoder-decoder Attention Mechanism Hearing Aid Usage

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Micro-climate Prediction - Multi Scale encoder-decoder based Deep Learning Framework 21

Micro-climate Prediction - Multi Scale Encoder-decoder based...

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27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)

作者： Kumar, Peeyush Chandra, Ranveer Bansal, Chetan Kalyanaraman, Shivkumar Ganu, Tanuja Grant, Michael Microsoft Res Redmond WA 98052 USA Microsoft Bengaluru India Microsoft Res Bengaluru India Univ Washington Seattle WA 98195 USA Climate Corp San Francisco CA USA

ISBN: (纸本)9781450383325

This paper presents a deep learning approach for a versatile Microclimate prediction framework (DeepMC). Micro climate predictions are of critical importance across various applications, such as Agriculture, Forestry, Energy, Search & Rescue, etc. To the best of our knowledge, there is no other single framework which can accurately predict various micro-climate entities using Internet of Things (IoT) data. We present a generic framework (DeepMC) which predicts various climatic parameters such as soil moisture, humidity, wind speed, radiation, temperature based on the requirement over a period of 12 hours - 120 hours with a varying resolution of 1 hour - 6 hours, respectively. This framework proposes the following new ideas: 1) Localization of weather forecast to IoT sensors by fusing weather station forecasts with the decomposition of IoT data at multiple scales and 2) A multi-scale encoder and two levels of attention mechanisms which learns a latent representation of the interaction between various resolutions of the IoT sensor data and weather station forecasts. We present multiple real-world agricultural and energy scenarios, and report results with uncertainty estimates from the live deployment of DeepMC, which demonstrate that DeepMC outperforms various baseline methods and reports 90%+ accuracy with tight error bounds.

关键词： Micro-climate prediction Sustainability Deep learning encoder-decoder Attention mechanism Transfer learning IoT sensors Sequence to sequence Time series Agriculture Energy Wavelet transform

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HYPERSPECTRAL IMAGE CLASSIFICATION VIA MULTI-SCALE encoder-decoder NETWORK

HYPERSPECTRAL IMAGE CLASSIFICATION VIA MULTI-SCALE ENCODER-D...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Ma, Jingjing Wu, Linlin Tang, Xu Zhang, Xiangrong Zhu, Cheng Ma, Junyong Jiao, Licheng Xidian Univ Sch Elect Engn Sch Artificial Intelligence Xian 710071 Shaanxi Peoples R China Sci & Technol Electroopt Control Lab Luoyang 471023 Peoples R China Xidian Univ Sch Artificial Intelligence Xian Peoples R China Xidian Univ Sch Elect Engn Xian Peoples R China

ISBN: (纸本)9781728163741

Hyperspectral image (HSI) classification is an important task in the remote sensing community. In general, many hyperspectral classification methods are based on pixel patch, which leads to information redundancy. In this paper, we propose a multi-scale encoder-decoder network for HSI classification. First, we adapt an encoder-decoder framework as the backbone network and use a skip connection between the encoder and decoder, the spatial information is obtained by this network. Second, we develop a multi-scale block to get the multi-scale information. Third, we retain complete spectral information through the constant number of spectral channels. Finally, an optimizer strategy is designed to achieve our model for the HSI classification task. We experiment with our method and other methods on two public datasets, and the results denote our model is useful for HSI classification task.

关键词： Hyperspectral image classification encoder-decoder multi-scale

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3D Multi-Branch encoder-decoder Networks with Attentional Feature Fusion for Pulmonary Nodule Detection in CT Scans

3D Multi-Branch Encoder-Decoder Networks with Attentional Fe...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhang, Chenjiao Wang, Lulu Wu, Xing He, Zhongshi Chongqing Univ Coll Comp Sci Chongqing Peoples R China

ISBN: (纸本)9780738133669

Pulmonary nodule detection in low-dose computed tomography (CT) images is essential for early screening and treatment of lung cancer. Previous related researches based on deep convolutional neural networks generally rely on 2D or 2.5D components and only focus on the output feature information under a single receptive field. Considering the 3D nature of lung CT images and the performance limitation of state-of-the-art nodule detection methods, we develop a novel 3D multi-branch region proposal network with an encoder-decoder structure. Specifically, each parallel branch is designed with 3D residual blocks and U-Net-like structure to effectively extract multi-scale fusion features based on 3D spatial information of CT scans, and the strategies of varying receptive fields and sharing weight parameters are used to improve the sensitivity of the detection network to nodules with scale variation and maintain the original parameters. Besides, we propose a multi-scale attentional feature fusion module to better fuse high-resolution and semantically strong features and adaptively learn the inter-dependency information of different feature maps. Finally, we compare a dynamically scaled cross entropy loss and online hard example mining (OHEM) to combat the imbalance of positive and negative samples during training, which is aimed at assisting with network optimization. Our extensive experiments on publicly available CT scans obtained from LUNA16 and TianChi(1) competition dataset demonstrate that our method outperform state-of-the-art pulmonary nodule detection models.

关键词： pulmonary nodule detection 3d convolutional neural network encoder-decoder multi-scale attentional feature fusion

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A GRU-based encoder-decoder Approach with Attention for Online Handwritten Mathematical Expression Recognition 14

A GRU-based Encoder-Decoder Approach with Attention for Onli...

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14th IAPR International Conference on Document Analysis and Recognition (ICDAR)

作者： Zhang, Jianshu Du, Jun Dai, Lirong Univ Sci & Technol China Natl Engn Lab Speech & Language Informat Proc Hefei Anhui Peoples R China

ISBN: (纸本)9781538635865

In this study, we present a novel end-to-end approach based on the encoder-decoder framework with the attention mechanism for online handwritten mathematical expression recognition (OHMER). First, the input two-dimensional ink trajectory information of handwritten expression is encoded via the gated recurrent unit based recurrent neural network (GRU-RNN). Then the decoder is also implemented by the GRU-RNN with a coverage-based attention model. The proposed approach can simultaneously accomplish the symbol recognition and structural analysis to output a character sequence in LaTeX format. Validated on the CROHME 2014 competition task, our approach significantly outperforms the state-of-the-art with an expression recognition accuracy of 52.43% by only using the official training dataset. Furthermore, the alignments between the input trajectories of handwritten expressions and the output LaTeX sequences are visualized by the attention mechanism to show the effectiveness of the proposed method.

关键词： Online Handwritten Mathematical Expression Recognition encoder-decoder Gated Recurrent Unit Attention

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Colorectal Segmentation using Multiple encoder-decoder Network in Colonoscopy Images 1

Colorectal Segmentation using Multiple Encoder-Decoder Netwo...

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1st IEEE International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)

作者： Ngoc-Quang Nguyen Lee, Sang-Woong Gachon Univ Pattern Recognit & Machine Learning Lab Seongnam South Korea

ISBN: (纸本)9781538695555

Colorectal cancer is the third most common cancer which causes of cancer-related deaths. Therefore, early diagnosis of polyps by colonoscopy could result in successful treatment. Diagnosis of polyps in colonoscopy videos is a challenging task due to variations in the size and shape of polyps. In this paper, we propose a polyp segmentation method based on the encoder decoder network. Performance of the method is enhanced by two strategies, we perform a novel database augmentation method for colonoscopy images in the training phase. Besides, in the test phase, we perform an effective prediction by combining multi model to compare the probability of each image that is produced by the network. Evaluation of the proposed method using the ETIS-LariPolypDB [9] database shows that our proposed method outperforms state-of-the-art results.

关键词： colorectal segmentation CRC encoder-decoder multi-model

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A Decomposition-based encoder-decoder Framework for Multi-step Prediction of Burn-Through Point in Sintering Process 6

A Decomposition-based Encoder-Decoder Framework for Multi-st...

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IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)

作者： Xie, Yuhan He, Bocun Zhang, Xinmin Song, Zhihuan Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou Peoples R China

ISBN: (纸本)9798350311259

Sintering process is a critical step in the ironmaking process. Burn-through point (BTP), as a key performance index of sintering ore, has a great influence on the quality of the sintering product. The existing prediction methods attempt to use a single model to establish the relationship between variables. However, due to the strong volatility, uncertainty, and multivariable coupling of sintering process, the traditional prediction model cannot produce reliable predictions. In order to deal with the complex characteristics of sintering process, this paper proposes a decomposition-based encoder-decoder modeling framework, in which a sequence decomposition module is designed to decompose the input time series into different sub-sequences. Then, these sub-sequences are constructed by the encoder-decoder models separately. The effectiveness of the proposed multi-step ahead prediction modeling framework was evaluated in a real-world sintering process. Compared with the traditional prediction modeling framework, the proposed modeling framework has more accurate results in multi-step ahead prediction.

关键词： Sintering process burn-through point multi-step ahead prediction encoder-decoder series decomposition

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AUTODEPTH: SINGLE IMAGE DEPTH MAP ESTIMATION VIA RESIDUAL CNN encoder-decoder AND STACKED HOURGLASS 26

AUTODEPTH: SINGLE IMAGE DEPTH MAP ESTIMATION VIA RESIDUAL CN...

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26th IEEE International Conference on Image Processing (ICIP)

作者： Kumari, Seema Jha, Ranjeet Ranjhan Bhavsar, Arnav Nigam, Aditya Indian Inst Technol Mandi Sch Comp & Elect Engn Mandi Himachal Prades India

ISBN: (纸本)9781538662496

We address the task of estimating depth from a single intensity image via a novel convolutional neural network (CNN) encoder-decoder architecture, which learns the depth information using example pairs of color images and their corresponding depth maps. The proposed model integrates residual connections within pooling and up-sampling layers, and hourglass networks which operate on the encoded features, thus processing these at various scales. Furthermore, the model is optimized under the constraints of perceptual as well as the mean squared error loss. The perceptual loss considers the high-level features, thus operating at a different scale of abstraction, which is complementary to the mean squared error loss. The improvements in qualitative and quantitative comparisons with state-of-the-art approaches demonstrate the effectiveness of our approach, even in presence of noise.

关键词： Depth map estimation CNN Residual connection encoder-decoder Hourglass

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Attention and encoder-decoder based models for transforming articulatory movements at different speaking rates 21

Attention and Encoder-Decoder based models for transforming ...

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Interspeech Conference

作者： Singh, Abhayjeet Illa, Aravind Ghosh, Prasanta Kumar Indian Inst Sci IISc Elect Engn Bangalore 560012 Karnataka India

ISBN: (纸本)9781713820697

While speaking at different rates, articulators (like tongue, lips) tend to move differently and the enunciations are also of different durations. In the past, affine transformation and DNN have been used to transform articulatory movements from neutral to fast(N2F) and neutral to slow(N2S) speaking rates [1]. In this work, we improve over the existing transformation techniques by modeling rate specific durations and their transformation using AstNet, an encoder-decoder framework with attention. In the current work, we propose an encoder-decoder architecture using LSTMs which generates smoother predicted articulatory trajectories. For modeling duration variations across speaking rates, we deploy attention network, which eliminates the need to align trajectories in different rates using DTW. We perform a phoneme specific duration analysis to examine how well duration is transformed using the proposed AstNet. As the range of articulatory motions is correlated with speaking rate, we also analyze amplitude of the transformed articulatory movements at different rates compared to their original counterparts, to examine how well the proposed AstNet predicts the extent of articulatory movements in N2F and N2S. We observe that AstNet could model both duration and extent of articulatory movements better than the existing transformation techniques resulting in more accurate transformed articulatory trajectories.

关键词： encoder-decoder Attention LSTM Speaking rate Electromagnetic Articulography

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encoder-decoder Architecture for 3D Seismic Inversion

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SENSORS 2023年第1期23卷 61页

作者： Gelboim, Maayan Adler, Amir Sun, Yen Araya-Polo, Mauricio Braude Coll Engn Elect Engn Dept IL-2161002 Karmiel Israel TotalEnergies EP R&T Houston TX 77002 USA

Inverting seismic data to build 3D geological structures is a challenging task due to the overwhelming amount of acquired seismic data, and the very-high computational load due to iterative numerical solutions of the wave equation, as required by industry-standard tools such as Full Waveform Inversion (FWI). For example, in an area with surface dimensions of 4.5 km x 4.5 km, hundreds of seismic shot-gather cubes are required for 3D model reconstruction, leading to Terabytes of recorded data. This paper presents a deep learning solution for the reconstruction of realistic 3D models in the presence of field noise recorded in seismic surveys. We implement and analyze a convolutional encoder-decoder architecture that efficiently processes the entire collection of hundreds of seismic shot-gather cubes. The proposed solution demonstrates that realistic 3D models can be reconstructed with a structural similarity index measure (SSIM) of 0.9143 (out of 1.0) in the presence of field noise at 10 dB signal-to-noise ratio.

关键词： 3D reconstruction seismic inversion seismic velocity inverse problems deep learning transfer learning encoder-decoder

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