95 citations · 206 across the 6 of their papers we have counts for
4 papers · 1 filter
Can the early human visual system compete with Deep Neural Networks?
Samuel Dodge, Lina Karam
We study and compare the human visual system and state-of-the-art deep neural networks on classification of distorted images. Different from previous works, we limit the display ti…
A Study and Comparison of Human and Deep Learning Recognition Performance Under Visual Distortions
Samuel Dodge, Lina Karam
Deep neural networks (DNNs) achieve excellent performance on standard classification tasks. However, under image quality distortions such as blur and noise, classification accuracy…
Quality Resilient Deep Neural Networks
Samuel Dodge, Lina Karam
We study deep neural networks for classification of images with quality distortions. We first show that networks fine-tuned on distorted data greatly outperform the original networ…
Visual Saliency Prediction Using a Mixture of Deep Neural Networks
Samuel Dodge, Lina Karam
Visual saliency models have recently begun to incorporate deep learning to achieve predictive capacity much greater than previous unsupervised methods. However, most existing model…