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05/06/2023
Weighted Point Cloud Normal Estimation
Existing normal estimation methods for point clouds are often less robus...
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04/25/2023
Retinal Vessel Segmentation via a Multi-resolution Contextual Network and Adversarial Learning
Timely and affordable computer-aided diagnosis of retinal diseases is pi...
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04/04/2023
IterativePFN: True Iterative Point Cloud Filtering
The quality of point clouds is often limited by noise introduced during ...
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02/21/2023
On the Behaviour of Pulsed Qubits and their Application to Feed Forward Networks
In the last two decades, the combination of machine learning and quantum...
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11/18/2022
DGD-cGAN: A Dual Generator for Image Dewatering and Restoration
Underwater images are usually covered with a blue-greenish colour cast, ...
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10/15/2022
MKIS-Net: A Light-Weight Multi-Kernel Network for Medical Image Segmentation
Image segmentation is an important task in medical imaging. It constitut...
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10/14/2022
Neural Network Compression by Joint Sparsity Promotion and Redundancy Reduction
Compression of convolutional neural network models has recently been dom...
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10/05/2022
Graph Classification via Discriminative Edge Feature Learning
Spectral graph convolutional neural networks (GCNNs) have been producing...
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09/13/2022
A Capsule Network for Hierarchical Multi-Label Image Classification
Image classification is one of the most important areas in computer visi...
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08/14/2022
Contrastive Learning for Joint Normal Estimation and Point Cloud Filtering
Point cloud filtering and normal estimation are two fundamental research...
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05/27/2022
Incorporating the Barzilai-Borwein Adaptive Step Size into Sugradient Methods for Deep Network Training
In this paper, we incorporate the Barzilai-Borwein step size into gradie...
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12/21/2021
RC-Net: A Convolutional Neural Network for Retinal Vessel Segmentation
Over recent years, increasingly complex approaches based on sophisticate...
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10/20/2021
Deep Point Cloud Normal Estimation via Triplet Learning
Normal estimation on 3D point clouds is a fundamental problem in 3D visi...
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10/12/2021
Robust Neural Regression via Uncertainty Learning
Deep neural networks tend to underestimate uncertainty and produce overl...
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09/23/2020
A Derivative-free Method for Quantum Perceptron Training in Multi-layered Neural Networks
In this paper, we present a gradient-free approach for training multi-la...
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08/14/2020
Feature-Extracting Functions for Neural Logic Rule Learning
In this paper, we present a method aimed at integrating domain knowledge...
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07/18/2020
Toward a Deep Learning-Driven Intrusion Detection Approach for Internet of Things
Internet of Things (IoT) has brought along immense benefits to our daily...
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07/11/2020
Deep Patch-based Human Segmentation
3D human segmentation has seen noticeable progress in re-cent years. It,...
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04/22/2020
Hierarchically Fair Federated Learning
Federated learning facilitates collaboration among self-interested agent...
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02/26/2020
Incorporating Expert Prior Knowledge into Experimental Design via Posterior Sampling
Scientific experiments are usually expensive due to complex experimental...
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09/05/2019
Super-resolved Chromatic Mapping of Snapshot Mosaic Image Sensors via a Texture Sensitive Residual Network
This paper introduces a novel method to simultaneously super-resolve and...
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04/01/2019
PIRM2018 Challenge on Spectral Image Super-Resolution: Dataset and Study
This paper introduces a newly collected and novel dataset (StereoMSI) fo...
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02/15/2019
Breaking the Spatio-Angular Trade-off for Light Field Super-Resolution via LSTM Modelling on Epipolar Plane Images
Light-field cameras (LFC) have received increasing attention due to thei...
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12/08/2017
A Frequency Domain Neural Network for Fast Image Super-resolution
In this paper, we present a frequency domain neural network for image su...
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06/18/2012