Results 251 to 260 of about 788,437 (264)

Learning from Residuals

2010
Residuals often are considered as a troublesome noise in spatial—or, for that matter—non-spatial econometric models. Current practice in spatial econometrics is to set up a spatial error model, more often than not with an exogenous W spatial weight matrix, in order to improve the efficiency of the estimators.
Daniel A. Griffith, Jean H.P. Paelinck
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Dictionary learning with residual codes

2017 25th Signal Processing and Communications Applications Conference (SIU), 2017
In conventional sparse representations based dictionary learning algorithms, initial dictionaries are generally assumed to be proper representatives of the system at hand. However, this may not be the case, especially in some systems restricted to random initialization.
Yigit Oktar, Mehmet Turkan
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Residual Learning Dehazing Net

2018
Single haze removal is a challenging ill-posed problem. Most existing methods solving this dilemma depend on atmospheric physical scattering model. In other words, they recover haze-free images by estimating the atmospheric transmission. In this paper, we proposed a new recovery model called Residual Adding model, which takes dehazing procedure as a ...
Yili Gu, Xinguang Xiang
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Deep Residual Attention Reinforcement Learning

2019 International Conference on Technologies and Applications of Artificial Intelligence (TAAI), 2019
Making decisions based more on the crucial objects which are closely connected to the reward in a given visual input is advantageous in reinforcement learning. In this work, we incorporate an attention-based structure into the network structure of Importance Weighted Actor-Learner Architecture (IMPALA) to help the model find out the crucial objects and
Hanhua Zhu, Tomoyuki Kaneko
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Residual Learning of Transfer-learned AlexNet for Image Denoising

IEIE Transactions on Smart Processing & Computing, 2020
In today’s scenarios, deep learning has fascinated all researchers from numerous arenas who developed ways to achieve obligatory outcomes. In deep learning, transfer learning is undergoing deep study, because the study helps to practice a pre-trained network for our own tasks. A novel, transfer-learned AlexNet-based residual learning for Gaussian noise
Mohan Laavanya, Veeramani Vijayaraghavan
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Attention Residual Learning for Skin Lesion Classification

IEEE Transactions on Medical Imaging, 2019
Automated skin lesion classification in dermoscopy images is an essential way to improve the diagnostic performance and reduce melanoma deaths. Although deep convolutional neural networks (DCNNs) have made dramatic breakthroughs in many image classification tasks, accurate classification of skin lesions remains challenging due to the insufficiency of ...
Jianpeng Zhang, Yutong Xie, Yong Xia, Chunhua Shen   +3 more
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Improved protein residue-residue contacts prediction using learning-to-rank

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2016
Protein residue-residue contacts dictate the topology of protein structure and play an important role in structural biology, especially in de novo protein structure prediction. Accurate prediction of residue contacts could improve the performance of de novo protein structure prediction methods.
Xiaoyang Jing, Qiwen Dong
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Deep residual learning for image steganalysis

Multimedia Tools and Applications, 2017
Image steganalysis is to discriminate innocent images and those suspected images with hidden messages. This task is very challenging for modern adaptive steganography, since modifications due to message hiding are extremely small. Recent studies show that Convolutional Neural Networks (CNN) have demonstrated superior performances than traditional ...
Songtao Wu, Shenghua Zhong, Yan Liu
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Residual Learning for Face Sketch Synthesis

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018
Face sketch synthesis plays an important role in both digital entertainment and law enforcement. It can bridge the great texture discrepancy between face photos and sketches. Most of the current face sketch synthesis approaches directly learn the relationship between the photos and sketches, and it is very difficult for them to generate the individual ...
Junjun Jiang, Yi Yu, Zheng Wang, Jiayi Ma   +3 more
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Invertible Residual Blocks in Deep Learning Networks

IEEE Transactions on Neural Networks and Learning Systems
Residual blocks have been widely used in deep learning networks. However, information may be lost in residual blocks due to the relinquishment of information in rectifier linear units (ReLUs). To address this issue, invertible residual networks have been proposed recently but are generally under strict restrictions which limit their applications.
Ruhua Wang, Senjian An, Wanquan Liu, Ling Li   +3 more
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