activity
20192022
most citedHow to Manipulate CNNs to Make Them Lie: the GradCAM Case

14 citations · 24 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2022

Enhancing Classifier Conservativeness and Robustness by Polynomiality

Ziqi Wang, Marco Loog

We illustrate the detrimental effect, such as overconfident decisions, that exponential behavior can have in methods like classical LDA and logistic regression. We then show how po…

cs.LG2020★ 8 cited

Respecting Domain Relations: Hypothesis Invariance for Domain Generalization

Ziqi Wang, Marco Loog, Jan van Gemert

In domain generalization, multiple labeled non-independent and non-identically distributed source domains are available during training while neither the data nor the labels of tar…

cs.CV2020

Black Magic in Deep Learning: How Human Skill Impacts Network Training

Kanav Anand, Ziqi Wang, Marco Loog +1

How does a user's prior experience with deep learning impact accuracy? We present an initial study based on 31 participants with different levels of experience. Their task is to pe…

cs.CV2019★ 2 cited

Attention-Aware Age-Agnostic Visual Place Recognition

Ziqi Wang, Jiahui Li, Seyran Khademi +1

A cross-domain visual place recognition (VPR) task is proposed in this work, i.e., matching images of the same architectures depicted in different domains. VPR is commonly treated…

cs.CV2019★ 14 cited

How to Manipulate CNNs to Make Them Lie: the GradCAM Case

Tom Viering, Ziqi Wang, Marco Loog +1

Recently many methods have been introduced to explain CNN decisions. However, it has been shown that some methods can be sensitive to manipulation of the input. We continue this li…