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To understand the black-box characteristics of deep networks, counterfactual explanation that deduces not only the important features of an input space but also how those features should be modified to classify input as a target class has gained an increasing interest.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting, Journal of Machine Learning Research 15 (2014) 1929–1958
1958
Earlier work this paper cites.
E. Rosenberg, A. Gleit, Quantitative methods in credit management: a survey, Operations Research 42 (4) (1994) 589–613
1994
Earlier work this paper cites.
Y. Lecun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition, in: Proceedings of the IEEE, 1998, pp. 2278–2324
1998
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition, Proceedings of the IEEE 86 (11) (1998) 2278–2324
1998
Earlier work this paper cites.
2008
Earlier work this paper cites.
I. C. Yeh, C. H. Lien, The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients, Expert Systems with Applications 36 (2) (2009) 2473–2480
2009
Earlier work this paper cites.
A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, C. Potts, Learning word vectors for sentiment analysis, in: Proceedings of the Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 2011, pp. 142–150
2011
Earlier work this paper cites.
J. Pennington, R. Socher, C. D. Manning, Glove: Global vectors for word representation, in: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), 2014, pp. 1532–1543
2014
Earlier work this paper cites.
S. Bach, A. Binder, G. Montavon, F. Klauschen, K. R. Müller, W. Samek, On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation, Plos One 10 (7) (2015) e0130140
2015
Earlier work this paper cites.
R. Garg, V. K. BG, G. Carneiro, I. Reid, Unsupervised cnn for single view depth estimation: Geometry to the rescue, in: Proceedings of the European Conference on Computer Vision, 2016, pp. 740–756
2016
Earlier work this paper cites.
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, A. Torralba, Learning deep features for discriminative localization, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 2921–2929
2016
Earlier work this paper cites.
M. T. Ribeiro, S. Singh, C. Guestrin, “why should i trust you?" explaining the predictions of any classifier, in: Proceedings of the International Conference on Knowledge Discovery and Data Mining, 2016, pp. 1135–1144
2016
Earlier work this paper cites.
H. Xu, Y. Gao, F. Yu, T. Darrell, End-to-end learning of driving models from large-scale video datasets, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 2174–2182
2017
Cited alongside, same era.
R. C. Fong, A. Vedaldi, Interpretable explanations of black boxes by meaningful perturbation, in: Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 3429–3437
2017
Cited alongside, same era.
P. Dabkowski, Y. Gal, Real time image saliency for black box classifiers, in: Advances in Neural Information Processing Systems, 2017, pp. 6967–6976
2017
Cited alongside, same era.
A. Shrikumar, P. Greenside, A. Kundaje, Learning important features through propagating activation differences, in: Proceedings of the International Conference on Machine Learning, 2017, pp. 3145–3153
2017
Cited alongside, same era.
T. Laugel, M.-J. Lesot, C. Marsala, X. Renard, M. Detyniecki, Comparison-based inverse classification for interpretability in machine learning, in: International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, 2018, pp. 100–111
2018
Later among the works it cites.
T. C. Hsu, S. T. Liou, Y. P. Wang, Y. S. Huang, et al., Enhanced recurrent neural network for combining static and dynamic features for credit card default prediction, in: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 2019, pp. 1572–1576
2019
Later among the works it cites.
Y. Goyal, Z. Wu, J. Ernst, D. Batra, D. Parikh, S. Lee, Counterfactual visual explanations, in: Proceedings of the International Conference on Machine Learning, 2019, pp. 2376–2384
2019
Later among the works it cites.
R. Fong, M. Patrick, A. Vedaldi, Understanding deep networks via extremal perturbations and smooth masks, in: Proceedings of the IEEE International Conference on Computer Vision, 2019, pp. 2950–2958
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M. Sundararajan, A. Taly, Q. Yan, Axiomatic attribution for deep networks, in: Proceedings of the International Conference on Machine Learning, 2017, pp. 3319–3328
2017
Cited alongside, same era.
G. Montavon, S. Lapuschkin, A. Binder, W. Samek, K.-R. Müller, Explaining nonlinear classification decisions with deep taylor decomposition, Pattern Recognition 65 (2017) 211–222
2017
Cited alongside, same era.
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, D. Batra, Grad-cam: Visual explanations from deep networks via gradient-based localization, in: Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 618–626
2017
Cited alongside, same era.
A. Dhurandhar, P. Y. Chen, R. Luss, C. C. Tu, P. Ting, K. Shanmugam, P. Das, Explanations based on the missing: Towards contrastive explanations with pertinent negatives, in: Advances in Neural Information Processing Systems, 2018, pp. 592–603
2018
Cited alongside, same era.
FICO, Explainable machine learning challenge, https://community.fico.com/s/explainable-machine-learning-challenge (2018)
2018
Cited alongside, same era.
A. Chattopadhay, A. Sarkar, P. Howlader, V. N. Balasubramanian, Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks, in: Proceedings fo the IEEE Winter Conference on Applications of Computer Vision, 2018, pp. 839–847
2018
Cited alongside, same era.
B. Zhou, Y. Sun, D. Bau, A. Torralba, Interpretable basis decomposition for visual explanation, in: Proceedings of the European Conference on Computer Vision, 2018, pp. 119–134
2018
Cited alongside, same era.
S. Wachter, B. Mittelstadt, C. Russell, Counterfactual explanations without opening the black box: Automated decisions and the gdpr, Harvard Journal of Law & Technology 31 (2)
Cited in the paper.
2019
Later among the works it cites.
J. Wagner, J. M. Kohler, T. Gindele, L. Hetzel, J. T. Wiedemer, S. Behnke, Interpretable and fine-grained visual explanations for convolutional neural networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 9097–9107
2019
Later among the works it cites.
C. Chen, O. Li, D. Tao, A. Barnett, C. Rudin, J. K. Su, This looks like that: deep learning for interpretable image recognition, in: Advances in Neural Information Processing Systems, 2019, pp. 8928–8939
2019
Later among the works it cites.
R. Guidotti, A. Monreale, S. Matwin, D. Pedreschi, Black box explanation by learning image exemplars in the latent feature space, in: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2019, pp. 189–205
2019
Later among the works it cites.
R. Guidotti, A. Monreale, F. Giannotti, D. Pedreschi, S. Ruggieri, F. Turini, Factual and counterfactual explanations for black box decision making, IEEE Intelligent Systems 34 (6) (2019) 14–23
2019
Later among the works it cites.
W.-J. Nam, S. Gur, J. Choi, L. Wolf, S.-W. Lee, Relative attributing propagation: Interpreting the comparative contributions of individual units in deep neural networks., in: Proceedings of the AAAI Conference on Artificial Intelligence, 2020, pp. 2501–2508
2020
Closest in time.
J. Kauffmann, K.-R. Müller, G. Montavon, Towards explaining anomalies: a deep taylor decomposition of one-class models, Pattern Recognition 101 (2020) 107198
2020
Closest in time.
D. Liu, L. Zhang, T. Luo, L. Tao, Y. Wu, Towards interpretable and robust hand detection via pixel-wise prediction, Pattern Recognition (2020) 107202
2020
Closest in time.