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This study addresses generating counterfactual explanations with multimodal information.
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng · 2015
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
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Chainer: a next-generation open source framework for deep learning
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Learning spatiotemporal features with 3d convolutional networks
D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri · 2015
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One-vs-each approximation to softmax for scalable estimation of probabilities
M. T. R. AUEB · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Generating visual explanations
L. A. Hendricks, Z. Akata, M. Rohrbach, J. Donahue, B. Schiele, and T. Darrell · 2016
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Top-down neural attention by excitation backprop
J. Zhang, Z. Lin, J. Brandt, X. Shen, and S. Sclaroff · 2016
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Learning deep features for discriminative localization
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba · 2016
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Real time image saliency for black box classifiers
P. Dabkowski and Y. Gal · 2017
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Online real time multiple spatiotemporal action localisation and prediction
G. Singh, S. Saha, M. Sapienza, P. Torr, and F. Cuzzolin · 2017
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Convnet architecture search for spatiotemporal feature learning
D. Tran, J. Ray, Z. Shou, S.-F. Chang, and M. Paluri · 2017
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Counterfactual explanations without opening the black box: Automated decisions and the gdpr
S. Wachter, B. Mittelstadt, and C. Russell · 2017
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Grounding visual explanations
L. Anne Hendricks, R. Hu, T. Darrell, and Z. Akata · 2018
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Learning to explain: An information-theoretic perspective on model interpretation
J. Chen, L. Song, M. J. Wainwright, and M. I. Jordan · 2018
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Ava: A video dataset of spatio-temporally localized atomic visual actions
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Interpretable explanations of black boxes by meaningful perturbation
R. C. Fong and A. Vedaldi · 2017
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Making the v in vqa matter: Elevating the role of image understanding in visual question answering
Y. Goyal, T. Khot, D. Summers-Stay, D. Batra, and D. Parikh · 2017
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Automatic differentiation in pytorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
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Right for the right reasons: Training differentiable models by constraining their explanations
A. S. Ross, M. C. Hughes, and F. Doshi-Velez · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra · 2017
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C. Gu, C. Sun, D. A. Ross, C. Vondrick, C. Pantofaru, Y. Li, S. Vijayanarasimhan, G. Toderici, S. Ricco, R. Sukthankar, C. Schmid, and J. Malik · 2018
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Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet?
K. Hara, H. Kataoka, and Y. Satoh · 2018
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Viewpoint-aware video summarization
A. Kanehira, L. Van Gool, Y. Ushiku, and T. Harada · 2018
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Multimodal explanations: Justifying decisions and pointing to the evidence
D. H. Park, L. A. Hendricks, Z. Akata, A. Rohrbach, B. Schiele, T. Darrell, and M. Rohrbach · 2018
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Interpretable basis decomposition for visual explanation
B. Zhou, Y. Sun, D. Bau, and A. Torralba · 2018
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Learning to explain with complemental examples
A. Kanehira and T. Harada · 2019
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