12 citations · 36 across the 9 of their papers we have counts for
9 papers
Discovering Limitations of Image Quality Assessments with Noised Deep Learning Image Sets
Wei Dai, Daniel Berleant
Image quality is important, and can affect overall performance in image processing and computer vision as well as for numerous other reasons. Image quality assessment (IQA) is cons…
Benchmarking Robustness of Deep Learning Classifiers Using Two-Factor Perturbation
Wei Dai, Daniel Berleant
Accuracies of deep learning (DL) classifiers are often unstable in that they may change significantly when retested on adversarial images, imperfect images, or perturbed images. Th…
CAN3D: Fast 3D Medical Image Segmentation via Compact Context Aggregation
Wei Dai, Boyeong Woo, Siyu Liu +6
Direct automatic segmentation of objects from 3D medical imaging, such as magnetic resonance (MR) imaging, is challenging as it often involves accurately identifying a number of in…
Benchmarking Robustness of Deep Learning Classifiers Using Two-Factor Perturbation
Wei Dai, Daniel Berleant
This paper adds to the fundamental body of work on benchmarking the robustness of deep learning (DL) classifiers. We innovate a new benchmarking methodology to evaluate robustness…
MC-ISTA-Net: Adaptive Measurement and Initialization and Channel Attention Optimization inspired Neural Network for Compressive Sensing
Nanyu Li, Cuiyin Liu
The optimization inspired network can bridge convex optimization and neural networks in Compressive Sensing (CS) reconstruction of natural image, like ISTA-Net+, which mapping opti…
A Comparison of deep learning methods for environmental sound
Juncheng Li, Wei Dai, Florian Metze +2
Environmental sound detection is a challenging application of machine learning because of the noisy nature of the signal, and the small amount of (labeled) data that is typically a…