Advanced School for Computing and Imaging (ASCI)

ASCI office
Delft University of Technology
Building 28, room 04.E120
Van Mourik Broekmanweg 6
2628 XE – DELFT, The Netherlands

P: +31 15 27 88032

Visiting hours office
Monday, Tuesday, Thursday: 10:00 – 15:00


The ASCI office is located at the Delft University of Technology campus.  It is easily accessible by bicycle, public transport and car. The numbers of buildings can help you find your way around the campus. Make sure you remember the name and building number of your destination.

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Explainable Robustness for Visual Classification


Author : Sadaf Gulshad
Promotor(s) : A.W.M. Smeulders
University : University of Amsterdam
Year of publication : 2022
Link to repository : Link to thesis

In this thesis, we explore the explainable robustness of neural networks for visual classification. We study an essential question for making neural networks deployable in real-world applications: “how to make neural networks explainably robust?” We start by making neural networks explainable. It begins with enabling black-box neural networks to justify their reasoning by leveraging attributes, i.e., visually discriminative properties of objects, and perturbations, to provide counterfactual explanations. The two chapters that follow focus on enhancing the robustness of neural networks against natural and adversarial perturbations. We do so by integrating perturbations in the network architecture and provide a rationale behind the modification of the network for enhancing its robustness by training the standard network with similarly transformed images. The last chapter utilizes attributes to improve robustness against perturbations and provides explanations as a byproduct.