most citedImbalanced Data Learning by Minority Class Augmentation using Capsule Adversarial Networks

9 citations · 16 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CV2022

Enhanced Single-shot Detector for Small Object Detection in Remote Sensing Images

Pourya Shamsolmoali, Masoumeh Zareapoor, Eric Granger +2

Small-object detection is a challenging problem. In the last few years, the convolution neural networks methods have been achieved considerable progress. However, the current detec…

cs.CV2020

Image Synthesis with Adversarial Networks: a Comprehensive Survey and Case Studies

Pourya Shamsolmoali, Masoumeh Zareapoor, Eric Granger +4

Generative Adversarial Networks (GANs) have been extremely successful in various application domains such as computer vision, medicine, and natural language processing. Moreover, t…

cs.CV20207 cited

Oversampling Adversarial Network for Class-Imbalanced Fault Diagnosis

Masoumeh Zareapoor, Pourya Shamsolmoali, Jie Yang

The collected data from industrial machines are often imbalanced, which poses a negative effect on learning algorithms. However, this problem becomes more challenging for a mixed t…

cs.LG20209 cited

Imbalanced Data Learning by Minority Class Augmentation using Capsule Adversarial Networks

Pourya Shamsolmoali, Masoumeh Zareapoor, Linlin Shen +2

The fact that image datasets are often imbalanced poses an intense challenge for deep learning techniques. In this paper, we propose a method to restore the balance in imbalanced i…

cs.CV2020

AMIL: Adversarial Multi Instance Learning for Human Pose Estimation

Pourya Shamsolmoali, Masoumeh Zareapoor, Huiyu Zhou +1

Human pose estimation has an important impact on a wide range of applications from human-computer interface to surveillance and content-based video retrieval. For human pose estima…

eess.IV2020

A novel Deep Structure U-Net for Sea-Land Segmentation in Remote Sensing Images

Pourya Shamsolmoali, Masoumeh Zareapoor, Ruili Wang +2

Sea-land segmentation is an important process for many key applications in remote sensing. Proper operative sea-land segmentation for remote sensing images remains a challenging is…