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Ruth Fong
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Cited by
Year
Interpretable explanations of black boxes by meaningful perturbation
RC Fong, A Vedaldi
IEEE International Conference on Computer Vision (ICCV), 2017
18882017
Understanding deep networks via extremal perturbations and smooth masks
R Fong, M Patrick, A Vedaldi
IEEE/CVF International Conference on Computer Vision (ICCV), 2950-2958, 2019
4882019
Toward trustworthy AI development: mechanisms for supporting verifiable claims
M Brundage, S Avin, J Wang, H Belfield, G Krueger, G Hadfield, H Khlaaf, ...
arXiv preprint arXiv:2004.07213, 2020
4102020
Net2vec: Quantifying and explaining how concepts are encoded by filters in deep neural networks
R Fong, A Vedaldi
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 8730-8738, 2018
2992018
Multi-modal self-supervision from generalized data transformations
M Patrick, Y Asano, P Kuznetsova, R Fong, JF Henriques, G Zweig, ...
1802020
There and back again: Revisiting backpropagation saliency methods
SA Rebuffi, R Fong, X Ji, A Vedaldi
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 8839-8848, 2020
1392020
" Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction
SSY Kim, EA Watkins, O Russakovsky, R Fong, A Monroy-Hernández
Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems …, 2023
1242023
Using human brain activity to guide machine learning
RC Fong, WJ Scheirer, DD Cox
Scientific reports 8 (1), 5397, 2018
1152018
HIVE: Evaluating the human interpretability of visual explanations
SSY Kim, N Meister, VV Ramaswamy, R Fong, O Russakovsky
European Conference on Computer Vision, 280-298, 2022
1002022
On compositions of transformations in contrastive self-supervised learning
M Patrick, YM Asano, P Kuznetsova, R Fong, JF Henriques, G Zweig, ...
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021
712021
Explanations for attributing deep neural network predictions
R Fong, A Vedaldi
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 149-167, 2019
672019
Overlooked factors in concept-based explanations: Dataset choice, concept learnability, and human capability
VV Ramaswamy, SSY Kim, R Fong, O Russakovsky
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023
412023
XxAI--Beyond Explainable AI: International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers
A Holzinger, R Goebel, R Fong, T Moon, KR Müller, W Samek
Springer Nature, 2022
38*2022
Contextual Semantic Interpretability
D Marcos, R Fong, S Lobry, R Flamary, N Courty, D Tuia
Asian Conference on Computer Vision (ACCV), 2020
352020
xxAI-Beyond Explainable Artificial Intelligence
A Holzinger, R Goebel, R Fong, T Moon, KR Müller, W Samek
International Workshop on Extending Explainable AI Beyond Deep Models and …, 2022
342022
Gender artifacts in visual datasets
N Meister, D Zhao, A Wang, VV Ramaswamy, R Fong, O Russakovsky
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023
282023
Occlusions for effective data augmentation in image classification
R Fong, A Vedaldi
IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) on …, 2019
212019
Humans, ai, and context: Understanding end-users’ trust in a real-world computer vision application
SSY Kim, EA Watkins, O Russakovsky, R Fong, A Monroy-Hernández
Proceedings of the 2023 ACM Conference on Fairness, Accountability, and …, 2023
202023
Quantifying Learnability and Describability of Visual Concepts Emerging in Representation Learning
I Laina, RC Fong, A Vedaldi
Neural Information Processing Systems (NeurIPS), 2020
142020
ELUDE: Generating interpretable explanations via a decomposition into labelled and unlabelled features
VV Ramaswamy, SSY Kim, N Meister, R Fong, O Russakovsky
arXiv preprint arXiv:2206.07690, 2022
92022
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Articles 1–20