activity
20212023
most citedMan-recon: manifold learning for reconstruction with deep autoencoder for smart seismic interpretation

15 citations · 25 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2023★ 10 cited

Explaining Deep Models through Forgettable Learning Dynamics

Ryan Benkert, Oluwaseun Joseph Aribido, Ghassan AlRegib

Even though deep neural networks have shown tremendous success in countless applications, explaining model behaviour or predictions is an open research problem. In this paper, we a…

cs.LG2022★ 15 cited

Man-recon: manifold learning for reconstruction with deep autoencoder for smart seismic interpretation

Ahmad Mustafa, Ghassan AlRegib

Deep learning can extract rich data representations if provided sufficient quantities of labeled training data. For many tasks however, annotating data has significant costs in ter…

cs.CV2022

Open-Set Recognition with Gradient-Based Representations

Jinsol Lee, Ghassan AlRegib

Neural networks for image classification tasks assume that any given image during inference belongs to one of the training classes. This closed-set assumption is challenged in real…

cs.CV2022

A novel attention model for salient structure detection in seismic volumes

Muhammad Amir Shafiq, Zhiling Long, Haibin Di +1

A new approach to seismic interpretation is proposed to leverage visual perception and human visual system modeling. Specifically, a saliency detection algorithm based on a novel a…

physics.geo-ph2021

Self-Supervised Delineation of Geological Structures using Orthogonal Latent Space Projection

Oluwaseun Joseph Aribido, Ghassan AlRegib, Yazeed Alaudah

We developed two machine learning frameworks that could assist in automated litho-stratigraphic interpretation of seismic volumes without any manual hand labeling from an experienc…