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Ruth Fong
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Interpretable explanations of black boxes by meaningful perturbation
RC Fong, A Vedaldi
IEEE International Conference on Computer Vision (ICCV), 2017
19672017
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
5152019
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
4312020
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
3092018
Multi-modal self-supervision from generalized data transformations
M Patrick, Y Asano, P Kuznetsova, R Fong, JF Henriques, G Zweig, ...
1872020
" 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
1462023
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
1452020
Using human brain activity to guide machine learning
RC Fong, WJ Scheirer, DD Cox
Scientific reports 8 (1), 5397, 2018
1252018
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
1142022
Explanations for attributing deep neural network predictions
R Fong, A Vedaldi
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 149-167, 2019
722019
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
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
532023
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
43*2022
Contextual Semantic Interpretability
D Marcos, R Fong, S Lobry, R Flamary, N Courty, D Tuia
Asian Conference on Computer Vision (ACCV), 2020
372020
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
332023
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
332022
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
242023
Occlusions for effective data augmentation in image classification
R Fong, A Vedaldi
IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) on …, 2019
232019
Quantifying Learnability and Describability of Visual Concepts Emerging in Representation Learning
I Laina, RC Fong, A Vedaldi
Neural Information Processing Systems (NeurIPS), 2020
152020
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
102022
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