Search Results - Parameter shared encoder-decoder

Noise adaptive spatiotemporal neural networks for deformation prediction of high rockfill dams by Wang, Zijian, Ma, Gang, Ai, Zhitao, Ding, Qianru, Zhou, Wei

“… Then, probabilistic prediction method obtains parameters through the linear layer for loss function, which improves…”
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Bayesian deep convolutional encoder–decoder networks for surrogate modeling and uncertainty quantification by Zhu, Yinhao, Zabaras, Nicholas

“…We are interested in the development of surrogate models for uncertainty quantification and propagation in problems governed by stochastic PDEs using a deep convolutional encoder…”
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Semantic Segmentation of Large-Scale Outdoor Point Clouds by Encoder–Decoder Shared MLPs with Multiple Losses by Rim, Beanbonyka, Lee, Ahyoung, Hong, Min

“… Therefore, this paper presents an encoder–decoder shared multi-layer perceptron (MLP) with multiple losses, to address an issue of this semantic segmentation…”
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A multitask encoder–decoder model for quality prediction in injection moulding by Muaz, Muhammad, Yu, Hanxin, Sung, Wai Lam, Liu, Chang, Drescher, Benny

“… Several studies are conducted on modelling the relationship between the process parameters e.g…”
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Bidirectional trained tree-structured decoder for Handwritten Mathematical Expression Recognition by Cheng, Hanbo, Liu, Chenyu, Hu, Pengfei, Zhang, Zhenrong, Ma, Jiefeng, Du, Jun

“…The Handwritten Mathematical Expression Recognition (HMER) task is a critical branch in the field of Optical Character Recognition (OCR). Recent studies have…”
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Learning dual-pixel alignment for defocus deblurring by Li, Yu, Yi, Yaling, Shu, Xinya, Ren, Dongwei, Li, Qince, Zuo, Wangmeng

“…–decoder with skip-connections, where two branches with shared parameters in the encoder are employed to extract and align deep features from left and right views, and one decoder is adopted to fuse…”
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ClarifyNet: A high-pass and low-pass filtering based CNN for single image dehazing by Susladkar, Onkar, Deshmukh, Gayatri, Nag, Subhrajit, Mantravadi, Ananya, Makwana, Dhruv, Ravichandran, Sujitha, R, Sai Chandra Teja, Chavhan, Gajanan H, Mohan, C Krishna, Mittal, Sparsh

“… Based on this observation, we present a shared-encoder multi-decoder model ClarifyNet which employs interconnected parallelization…”
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Spatio-temporal multi-task network cascade for accurate assessment of cardiac CT perfusion by Chen, Jiaqi, Zhang, Pengfei, Liu, Huafeng, Xu, Lei, Zhang, Heye

“…•A spatio-temporal encoder-decoder architecture based on pseudo-3D convolutions is proposed to capture patch-based spatio-temporal representations…”
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A RGB-Thermal Image Segmentation Method Based on Parameter Sharing and Attention Fusion for Safe Autonomous Driving by Li, Guofa, Lin, Yongjie, Ouyang, Delin, Li, Shen, Luo, Xiao, Qu, Xingda, Pi, Dawei, Li, Shengbo Eben

“… An encoder-decoder network structure is adopted. The encoder, which has shared convolution layer parameters and private batch normalization layer parameters…”
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RPDNet: A reconstruction-regularized parallel decoders network for rectal tumor and rectum co-segmentation by Huang, WenXiang, Xu, Ye, Wang, Yuanyuan, Zheng, Hongtu, Guo, Yi

“… Only a few studies have explored deep learning networks in rectal tumor segmentation, which mainly adopt the classical encoder-decoder structure…”
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Radiology report generation via visual-semantic ambivalence-aware network and focal self-critical sequence training by Yi, Xiulong, Fu, You, Bi, Enxu, Liang, Jianguo, Zhang, Hao, Yu, Jianzhi, Li, Qianqian, Hua, Rong, Wang, Rui

“…) and the Focal Self-Critical Sequence Training (FSCST). In detail, our VSANet follows the encoder-decoder framework…”
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SHAPED: Shared-Private Encoder-Decoder for Text Style Adaptation by Zhang, Ye, Ding, Nan, Soricut, Radu

“… by the needs of an application. We describe a family of model architectures capable of capturing both generic language characteristics via shared model parameters, as well as particular style characteristics via private model parameters…”
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Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification by Zhu, Yinhao, Zabaras, Nicholas

“…We are interested in the development of surrogate models for uncertainty quantification and propagation in problems governed by stochastic PDEs using a deep convolutional encoder-decoder network…”
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Phrase embedding learning from internal and external information based on autoencoder by Li, Rongsheng, Yu, Qinyong, Huang, Shaobin, Shen, Linshan, Wei, Chi, Sun, Xuewei

“…•We propose an autoencoder-based method to generate phrase embedding.•The method uses full connected network, LSTM and attention to get phrase embedding.•The…”
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Image super-resolution based on conditional generative adversarial network by Gao, Hongxia, Chen, Zhanhong, Huang, Binyang, Chen, Jiahe, Li, Zhifu

“… The generator of cGAN is an encoder–decoder with skip connections to shuttle…”
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Domain Adaptation Networks With Parameter-Free Adaptively Rectified Linear Units for Fault Diagnosis Under Variable Operating Conditions by Chen, Yongyi, Zhang, Dan, Yan, Ruqiang

“…, parameter-free adaptively rectified linear units (PfAReLU). The proposed activation function performs adaptive nonlinear transformations according to the input data…”
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AiOENet: All-in-One Low-Visibility Enhancement to Improve Visual Perception for Intelligent Marine Vehicles Under Severe Weather Conditions by Liu, Ryan Wen, Lu, Yuxu, Guo, Yu, Ren, Wenqi, Zhu, Fenghua, Lv, Yisheng

“…Benefiting from the higher performance-cost ratio and installation convenience, the visible-light imaging camera has become one of the most widely-used onboard…”
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A robust watermarking algorithm against JPEG compression based on multiscale autoencoder by Zhang, Wei, Chen, Rongrong, Wang, Bin

“… At the same time, some parameters of decoder and encoder are shared to reduce redundant feature embedding and improve extraction accuracy…”
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SACNet: A Spatially Adaptive Convolution Network for 2D Multi-organ Medical Segmentation by Zhang, Lin, Gao, Wenbo, Yi, Jie, Yang, Yunyun

“…Multi-organ segmentation in medical image analysis is crucial for diagnosis and treatment planning. However, many factors complicate the task, including…”
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CrackUDA: Incremental Unsupervised Domain Adaptation for Improved Crack Segmentation in Civil Structures by Srivastava, Kushagra, Damodar Datta Kancharla, Rizvi Tahereen, Ramancharla, Pradeep Kumar, Sarvadevabhatla, Ravi Kiran, Kandath, Harikumar

“… Our approach leverages an encoder-decoder architecture, consisting of both domain-invariant and domain-specific parameters…”
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