16 citations · 20 across the 8 of their papers we have counts for
6 papers · 1 filter
Learning to Harmonize Cross-vendor X-ray Images by Non-linear Image Dynamics Correction
Yucheng Lu, Shunxin Wang, Dovile Juodelyte +1
In this paper, we explore how conventional image enhancement can improve model robustness in medical image analysis. By applying commonly used normalization methods to images from…
Do ImageNet-trained models learn shortcuts? The impact of frequency shortcuts on generalization
Shunxin Wang, Raymond Veldhuis, Nicola Strisciuglio
Frequency shortcuts refer to specific frequency patterns that models heavily rely on for correct classification. Previous studies have shown that models trained on small image data…
Dynamic Sparse Training versus Dense Training: The Unexpected Winner in Image Corruption Robustness
Boqian Wu, Qiao Xiao, Shunxin Wang +5
It is generally perceived that Dynamic Sparse Training opens the door to a new era of scalability and efficiency for artificial neural networks at, perhaps, some costs in accuracy…
Fourier-basis Functions to Bridge Augmentation Gap: Rethinking Frequency Augmentation in Image Classification
Puru Vaish, Shunxin Wang, Nicola Strisciuglio
Computer vision models normally witness degraded performance when deployed in real-world scenarios, due to unexpected changes in inputs that were not accounted for during training.…
DFM-X: Augmentation by Leveraging Prior Knowledge of Shortcut Learning
Shunxin Wang, Christoph Brune, Raymond Veldhuis +1
Neural networks are prone to learn easy solutions from superficial statistics in the data, namely shortcut learning, which impairs generalization and robustness of models. We propo…
A Survey on the Robustness of Computer Vision Models against Common Corruptions
Shunxin Wang, Raymond Veldhuis, Christoph Brune +1
The performance of computer vision models are susceptible to unexpected changes in input images caused by sensor errors or extreme imaging environments, known as common corruptions…