Search Results for author: Sadaf Gulshad

Found 7 papers, 3 papers with code

Wiggling Weights to Improve the Robustness of Classifiers

no code implementations18 Nov 2021 Sadaf Gulshad, Ivan Sosnovik, Arnold Smeulders

To demonstrate that wiggling the weights consistently improves classification, we choose a standard network and modify it to a transform-augmented network.

Built-in Elastic Transformations for Improved Robustness

no code implementations20 Jul 2021 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.

Data Augmentation

Natural Perturbed Training for General Robustness of Neural Network Classifiers

no code implementations21 Mar 2021 Sadaf Gulshad, Arnold Smeulders

For Cifar-10 and STL-10 natural perturbed training even improves the accuracy for clean data and reaches the state of the art performance.

Adversarial and Natural Perturbations for General Robustness

no code implementations3 Oct 2020 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.

Explaining with Counter Visual Attributes and Examples

1 code implementation27 Jan 2020 Sadaf Gulshad, Arnold Smeulders

Hence, inspired by the way of human explanations in this paper we provide attribute-based and example-based explanations.

Understanding Misclassifications by Attributes

1 code implementation15 Oct 2019 Sadaf Gulshad, Zeynep Akata, Jan Hendrik Metzen, Arnold Smeulders

We study the changes in attributes for clean as well as adversarial images in both standard and adversarially robust networks.

Interpreting Adversarial Examples with Attributes

1 code implementation17 Apr 2019 Sadaf Gulshad, Jan Hendrik Metzen, Arnold Smeulders, Zeynep Akata

Deep computer vision systems being vulnerable to imperceptible and carefully crafted noise have raised questions regarding the robustness of their decisions.

General Classification

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