Ruth Fong
Title
Cited by
Cited by
Year
Interpretable explanations of black boxes by meaningful perturbation
RC Fong, A Vedaldi
The IEEE International Conference on Computer Vision (ICCV), 2017
5152017
Net2vec: Quantifying and explaining how concepts are encoded by filters in deep neural networks
R Fong, A Vedaldi
Proceedings of the IEEE conference on computer vision and pattern …, 2018
752018
Using human brain activity to guide machine learning
RC Fong, WJ Scheirer, DD Cox
Scientific reports 8 (1), 1-10, 2018
412018
Understanding deep networks via extremal perturbations and smooth masks
R Fong, M Patrick, A Vedaldi
Proceedings of the IEEE International Conference on Computer Vision, 2950-2958, 2019
402019
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
222020
Multi-modal self-supervision from generalized data transformations
M Patrick, YM Asano, R Fong, JF Henriques, G Zweig, A Vedaldi
arXiv preprint arXiv:2003.04298, 2020
182020
Explanations for attributing deep neural network predictions
R Fong, A Vedaldi
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 149-167, 2019
162019
There and Back Again: Revisiting Backpropagation Saliency Methods
SA Rebuffi, R Fong, X Ji, A Vedaldi
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
52020
Interpretable explanations of black boxes by meaningful perturbation. CoRR abs/1704.03296 (2017)
R Fong, A Vedaldi
arXiv preprint arXiv:1704.03296, 2017
52017
NormGrad: Finding the Pixels that Matter for Training
SA Rebuffi, R Fong, X Ji, H Bilen, A Vedaldi
arXiv preprint arXiv:1910.08823, 2019
12019
Debiasing Convolutional Neural Networks via Meta Orthogonalization
KE David, Q Liu, R Fong
arXiv preprint arXiv:2011.07453, 2020
2020
Understanding deep networks via extremal perturbations and smooth masks
A Vedaldi, R Fong, M Patrick
IEEE, 2020
2020
Quantifying Learnability and Describability of Visual Concepts Emerging in Representation Learning
I Laina, R Fong, A Vedaldi
Advances in Neural Information Processing Systems 33, 2020
2020
Contextual Semantic Interpretability
D Marcos, R Fong, S Lobry, R Flamary, N Courty, D Tuia
Proceedings of the Asian Conference on Computer Vision, 2020
2020
Occlusions for Effective Data Augmentation in Image Classification
R Fong
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW …, 2019
2019
Kuramoto Model Simulation
R Fong, J Russell, G Weerasinghe, R Bogacz
University of Oxford, 2018
2018
Supplementary Materials for “Interpretable Explanations of Black Boxes by Meaningful Perturbation”
RC Fong, A Vedaldi
2017
Optimizing and Modeling Phase-Locked Deep Brain Stimulation to Suppress Tremor
R Fong
2016
Leveraging Human Brain Activity to Improve Object Classification
RC Fong
2015
Supplementary Materials for ‘Net2Vec: Quantifying and Explaining how Concepts are Encoded by Filters in Deep Neural Networks’
R Fong, A Vedaldi
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Articles 1–20