58 citations · 175 across the 25 of their papers we have counts for
7 papers · 1 filter
DGD-cGAN: A Dual Generator for Image Dewatering and Restoration
Salma Gonzalez-Sabbagh, Antonio Robles-Kelly, Shang Gao
Underwater images are usually covered with a blue-greenish colour cast, making them distorted, blurry or low in contrast. This phenomenon occurs due to the light attenuation given…
MKIS-Net: A Light-Weight Multi-Kernel Network for Medical Image Segmentation
Tariq M. Khan, Muhammad Arsalan, Antonio Robles-Kelly +1
Image segmentation is an important task in medical imaging. It constitutes the backbone of a wide variety of clinical diagnostic methods, treatments, and computer-aided surgeries.…
Neural Network Compression by Joint Sparsity Promotion and Redundancy Reduction
Tariq M. Khan, Syed S. Naqvi, Antonio Robles-Kelly +1
Compression of convolutional neural network models has recently been dominated by pruning approaches. A class of previous works focuses solely on pruning the unimportant filters to…
Graph Classification via Discriminative Edge Feature Learning
Yang Yi, Xuequan Lu, Shang Gao +2
Spectral graph convolutional neural networks (GCNNs) have been producing encouraging results in graph classification tasks. However, most spectral GCNNs utilize fixed graphs when a…
A Capsule Network for Hierarchical Multi-Label Image Classification
Khondaker Tasrif Noor, Antonio Robles-Kelly, Brano Kusy
Image classification is one of the most important areas in computer vision. Hierarchical multi-label classification applies when a multi-class image classification problem is arran…
Contrastive Learning for Joint Normal Estimation and Point Cloud Filtering
Dasith de Silva Edirimuni, Xuequan Lu, Gang Li +1
Point cloud filtering and normal estimation are two fundamental research problems in the 3D field. Existing methods usually perform normal estimation and filtering separately and o…
Incorporating the Barzilai-Borwein Adaptive Step Size into Sugradient Methods for Deep Network Training
Antonio Robles-Kelly, Asef Nazari
In this paper, we incorporate the Barzilai-Borwein step size into gradient descent methods used to train deep networks. This allows us to adapt the learning rate using a two-point…