Search Results for author: Antonio Robles-Kelly

Found 23 papers, 3 papers with code

Weighted Point Cloud Normal Estimation

no code implementations6 May 2023 Weijia Wang, Xuequan Lu, Di Shao, Xiao Liu, Richard Dazeley, Antonio Robles-Kelly, Wei Pan

Existing normal estimation methods for point clouds are often less robust to severe noise and complex geometric structures.

Contrastive Learning regression

IterativePFN: True Iterative Point Cloud Filtering

1 code implementation CVPR 2023 Dasith de Silva Edirimuni, Xuequan Lu, Zhiwen Shao, Gang Li, Antonio Robles-Kelly, Ying He

Consequently, a fundamental 3D vision task is the removal of noise, known as point cloud filtering or denoising.

Denoising

On the Behaviour of Pulsed Qubits and their Application to Feed Forward Networks

no code implementations21 Feb 2023 Matheus Moraes Hammes, Antonio Robles-Kelly

In the last two decades, the combination of machine learning and quantum computing has been an ever-growing topic of interest but, to this date, the limitations of quantum computing hardware have somewhat restricted the use of complex multi-qubit operations for machine learning.

DGD-cGAN: A Dual Generator for Image Dewatering and Restoration

1 code implementation18 Nov 2022 Salma Gonzalez-Sabbagh, Antonio Robles-Kelly, Shang Gao

Our Dual Generator Dewatering cGAN (DGD-cGAN) removes the haze and colour cast induced by the water column and restores the true colours of underwater scenes whereby the effects of various attenuation and scattering phenomena that occur in underwater images are tackled by the two generators.

Generative Adversarial Network Image Enhancement

Graph Classification via Discriminative Edge Feature Learning

no code implementations5 Oct 2022 Yang Yi, Xuequan Lu, Shang Gao, Antonio Robles-Kelly, Yuejie Zhang

Three new graph datasets are constructed based on ModelNet40, ModelNet10 and ShapeNet Part datasets.

Graph Classification

Contrastive Learning for Joint Normal Estimation and Point Cloud Filtering

1 code implementation14 Aug 2022 Dasith de Silva Edirimuni, Xuequan Lu, Gang Li, Antonio Robles-Kelly

Existing methods usually perform normal estimation and filtering separately and often show sensitivity to noise and/or inability to preserve sharp geometric features such as corners and edges.

Contrastive Learning

Incorporating the Barzilai-Borwein Adaptive Step Size into Sugradient Methods for Deep Network Training

no code implementations27 May 2022 Antonio Robles-Kelly, Asef Nazari

In this paper, we incorporate the Barzilai-Borwein step size into gradient descent methods used to train deep networks.

RC-Net: A Convolutional Neural Network for Retinal Vessel Segmentation

no code implementations21 Dec 2021 Tariq M Khan, Antonio Robles-Kelly, Syed S. Naqvi

Over recent years, increasingly complex approaches based on sophisticated convolutional neural network architectures have been slowly pushing performance on well-established benchmark datasets.

Retinal Vessel Segmentation Segmentation

Deep Point Cloud Normal Estimation via Triplet Learning

no code implementations20 Oct 2021 Weijia Wang, Xuequan Lu, Dasith de Silva Edirimuni, Xiao Liu, Antonio Robles-Kelly

It consists of two phases: (a) feature encoding which learns representations of local patches, and (b) normal estimation that takes the learned representation as input and regresses the normal vector.

Robust Neural Regression via Uncertainty Learning

no code implementations12 Oct 2021 Akib Mashrur, Wei Luo, Nayyar A. Zaidi, Antonio Robles-Kelly

Deep neural networks tend to underestimate uncertainty and produce overly confident predictions.

regression

A Derivative-free Method for Quantum Perceptron Training in Multi-layered Neural Networks

no code implementations23 Sep 2020 Tariq M. Khan, Antonio Robles-Kelly

In this paper, we present a gradient-free approach for training multi-layered neural networks based upon quantum perceptrons.

Computational Efficiency

Feature Extraction Functions for Neural Logic Rule Learning

no code implementations14 Aug 2020 Shashank Gupta, Antonio Robles-Kelly, Mohamed Reda Bouadjenek

Combining symbolic human knowledge with neural networks provides a rule-based ante-hoc explanation of the output.

Sentiment Analysis Sentiment Classification

Hierarchically Fair Federated Learning

no code implementations22 Apr 2020 Jingfeng Zhang, Cheng Li, Antonio Robles-Kelly, Mohan Kankanhalli

When the federated learning is adopted among competitive agents with siloed datasets, agents are self-interested and participate only if they are fairly rewarded.

Fairness Federated Learning +1

Super-resolved Chromatic Mapping of Snapshot Mosaic Image Sensors via a Texture Sensitive Residual Network

no code implementations5 Sep 2019 Mehrdad Shoeiby, Lars Petersson, Mohammad Ali Armin, Sadegh Aliakbarian, Antonio Robles-Kelly

This paper introduces a novel method to simultaneously super-resolve and colour-predict images acquired by snapshot mosaic sensors.

PIRM2018 Challenge on Spectral Image Super-Resolution: Dataset and Study

no code implementations1 Apr 2019 Mehrdad Shoeiby, Antonio Robles-Kelly, Ran Wei, Radu Timofte

This paper introduces a newly collected and novel dataset (StereoMSI) for example-based single and colour-guided spectral image super-resolution.

Image Super-Resolution

Breaking the Spatio-Angular Trade-off for Light Field Super-Resolution via LSTM Modelling on Epipolar Plane Images

no code implementations15 Feb 2019 Hao Zhu, Mantang Guo, Hongdong Li, Qing Wang, Antonio Robles-Kelly

We prove that the light field is a 2D series, thus, a specifically designed CNN-LSTM network is proposed to capture the continuity property of the EPI.

Super-Resolution

A Frequency Domain Neural Network for Fast Image Super-resolution

no code implementations8 Dec 2017 Junxuan Li, ShaoDi You, Antonio Robles-Kelly

Moreover, the non-linearity in deep nets, often achieved by a rectifier unit, is here cast as a convolution in the frequency domain.

Image Super-Resolution

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