6 citations · 10 across the 5 of their papers we have counts for
6 papers
Built-in Elastic Transformations for Improved Robustness
Sadaf Gulshad, Ivan Sosnovik, Arnold Smeulders
We focus on building robustness in the convolutions of neural visual classifiers, especially against natural perturbations like elastic deformations, occlusions and Gaussian noise.…
Natural Perturbed Training for General Robustness of Neural Network Classifiers
Sadaf Gulshad, Arnold Smeulders
We focus on the robustness of neural networks for classification. To permit a fair comparison between methods to achieve robustness, we first introduce a standard based on the mens…
Adversarial and Natural Perturbations for General Robustness
Sadaf Gulshad, Jan Hendrik Metzen, Arnold Smeulders
In this paper we aim to explore the general robustness of neural network classifiers by utilizing adversarial as well as natural perturbations. Different from previous works which…
Explaining with Counter Visual Attributes and Examples
Sadaf Gulshad, Arnold Smeulders
In this paper, we aim to explain the decisions of neural networks by utilizing multimodal information. That is counter-intuitive attributes and counter visual examples which appear…
Understanding Misclassifications by Attributes
Sadaf Gulshad, Zeynep Akata, Jan Hendrik Metzen +1
In this paper, we aim to understand and explain the decisions of deep neural networks by studying the behavior of predicted attributes when adversarial examples are introduced. We…
Interpreting Adversarial Examples with Attributes
Sadaf Gulshad, Jan Hendrik Metzen, Arnold Smeulders +1
Deep computer vision systems being vulnerable to imperceptible and carefully crafted noise have raised questions regarding the robustness of their decisions. We take a step back an…