Results 21 to 30 of about 792,416 (284)
ResDepth: Learned Residual Stereo Reconstruction [PDF]
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020 We propose an embarrassingly simple but very effective scheme for high-quality dense stereo reconstruction: (i) generate an approximate reconstruction with your favourite stereo matcher; (ii) rewarp the input images with that approximate model; (iii) with the initial reconstruction and the warped images as input, train a deep network to enhance the ...
Stucker, Corinne, Schindler, Konrad
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RNTR-Net: A Robust Natural Text Recognition Network
IEEE Access, 2020 In this work, a novel robust natural text recognition network (RNTR-Net) is proposed based on a combination of convolutional neural network (CNN) (for feature extraction) and a recurrent neural network (RNN) (for sequence recognition).
Qiaokang Liang +4 more
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Deep Feature Aggregation Network for Hyperspectral Remote Sensing Image Classification
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 Hyperspectral remote sensing images (HSIs) are rich in spectral-spatial information. The deep learning models can help to automatically extract and discover this rich information from HSIs for classifying HSIs. However, the sampling of the models and the
Chunju Zhang +6 more
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Enhanced CNN for image denoising
CAAI Transactions on Intelligence Technology, 2019 Owing to the flexible architectures of deep convolutional neural networks (CNNs) are successfully used for image denoising. However, they suffer from the following drawbacks: (i) deep network architecture is very difficult to train.
Chunwei Tian +5 more
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Mathematics, 2021 Smart multimedia-based medical analytics and decision-making systems are of prime importance in the healthcare sector. Liver cancer is commonly stated to be the sixth most widely diagnosed cancer and requires an early diagnosis to help with treatment ...
Muazzam Maqsood +6 more
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Residual Recurrent Neural Networks for Learning Sequential Representations
Information, 2018 Recurrent neural networks (RNN) are efficient in modeling sequences for generation and classification, but their training is obstructed by the vanishing and exploding gradient issues.
Boxuan Yue, Junwei Fu, Jun Liang
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, 2020 We study a new form of federated learning where the clients train personalized local models and make predictions jointly with the server-side shared model. Using this new federated learning framework, the complexity of the central shared model can be minimized while still gaining all the performance benefits that joint training provides.
Agarwal, Alekh +2 more
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NCNet: Deep Learning Network Models for Predicting Function of Non-coding DNA
Frontiers in Genetics, 2019 The human genome consists of 98.5% non-coding DNA sequences, and most of them have no known function. However, a majority of disease-associated variants lie in these regions. Therefore, it is critical to predict the function of non-coding DNA.
Hanyu Zhang +8 more
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Remote Sensing, 2023 The analysis and evaluation of landslide susceptibility are of great significance in preventing and managing geological hazards. Aiming at the problems of insufficient information caused by the limited number of landslide datasets, complex information of
Yu Ma +7 more
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Learning Probabilistic Automata Using Residuals
, 2021 A probabilistic automaton is a non-deterministic finite automaton with probabilities assigned to transitions and states that define a distribution on the set of all strings. In general, there are distributions generated by automata with a non-deterministic structure that cannot be generated by a deterministic one. There exist several methods in machine
Chu, W., Chen, S., Bonsangue, M.M.
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