| Interpretable explanations of black boxes by meaningful perturbation RC Fong, A Vedaldi IEEE International Conference on Computer Vision (ICCV), 2017 | 1824 | 2017 |
| 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 | 463 | 2019 |
| 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 | 378 | 2020 |
| 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 | 281 | 2018 |
| Multi-modal self-supervision from generalized data transformations M Patrick, Y Asano, P Kuznetsova, R Fong, JF Henriques, G Zweig, ... | 177 | 2020 |
| 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 | 133 | 2020 |
| " 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 | 110 | 2023 |
| Using human brain activity to guide machine learning RC Fong, WJ Scheirer, DD Cox Scientific reports 8 (1), 5397, 2018 | 110 | 2018 |
| 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 | 88 | 2022 |
| 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 | 69 | 2021 |
| Explanations for attributing deep neural network predictions R Fong, A Vedaldi Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 149-167, 2019 | 63 | 2019 |
| 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 | 36 | 2023 |
| Contextual Semantic Interpretability D Marcos, R Fong, S Lobry, R Flamary, N Courty, D Tuia Asian Conference on Computer Vision (ACCV), 2020 | 30 | 2020 |
| 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 | 29 | 2022 |
| 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 | 27 | 2022 |
| 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 | 25 | 2023 |
| Occlusions for effective data augmentation in image classification R Fong, A Vedaldi IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) on …, 2019 | 21 | 2019 |
| 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 | 18 | 2023 |
| Quantifying Learnability and Describability of Visual Concepts Emerging in Representation Learning I Laina, RC Fong, A Vedaldi Neural Information Processing Systems (NeurIPS), 2020 | 14 | 2020 |
| 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 | 9 | 2022 |
Andrea VedaldiUniversity of Oxford確認したメール アドレス: robots.ox.ac.uk
