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Deep neural networks that yield human interpretable decisions by architectural design have lately become an increasingly popular alternative to post hoc interpretation of traditional black-box models.
JPEG Still Image Data Compression Standard
Pennebaker, W. B. and Mitchell, J. L · 1992
Earlier work this paper cites.
Influence of multichannel combination, parallel imaging and other reconstruction techniques on mri noise characteristics
Dietrich, O., Raya, J., Reeder, S., Ingrisch, M., Reiser, M., and Schoenberg, S · 2008
Earlier work this paper cites.
The Caltech-UCSD Birds-200-2011 Dataset
Wah, C., Branson, S., Welinder, P., Perona, P., and Belongie, S · 2011
Earlier work this paper cites.
Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2014
Earlier work this paper cites.
On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., and Samek, W · 2015
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Goodfellow, I., Shlens, J., and Szegedy, C · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Network dissection: Quantifying interpretability of deep visual representations
Bau, D., Zhou, B., Khosla, A., Oliva, A., and Torralba, A · 2017
Earlier work this paper cites.
Attentional pooling for action recognition
Girdhar, R. and Ramanan, D · 2017
Earlier work this paper cites.
Threat of adversarial attacks on deep learning in computer vision: A survey
Akhtar, N. and Mian, A · 2018
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Explaining explanations: An overview of interpretability of machine learning
Gilpin, L. H., Bau, D., Yuan, B. Z., Bajwa, A., Specter, M., and Kagal, L · 2018
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Noise issues prevailing in various types of medical images
Goyal, B., Dogra, A., Agrawal, S., and Sohi, B · 2018
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Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)
Kim, B., Wattenberg, M., Gilmer, J., Cai, C., Wexler, J., Viegas, F., et al · 2018
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Deep learning for case-based reasoning through prototypes: A neural network that explains its predictions
Li, O., Liu, H., Chen, C., and Rudin, C · 2018
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Unmasking clever hans predictors and assessing what machines really learn
Lapuschkin, S., Wäldchen, S., Binder, A., Montavon, G., Samek, W., and Müller, K.-R · 2019
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Interpretable and steerable sequence learning via prototypes
Ming, Y., Xu, P., Qu, H., and Ren, L · 2019
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PyTorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
Later among the works it cites.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Rudin, C · 2019
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Towards explainable artificial intelligence
Samek, W. and Müller, K.-R · 2019
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Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
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Approximating CNNs with bag-of-local-features models works surprisingly well on imagenet
Brendel, W. and Bethge, M · 2019
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This looks like that: Deep learning for interpretable image recognition
Chen, C., Li, O., Tao, D., Barnett, A., Rudin, C., and Su, J. K · 2019
Cited alongside, same era.
Explaining deep classification of time-series data with learned prototypes
Gee, A. H., García-Olano, D., Ghosh, J., and Paydarfar, D · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T · 2019
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Tsipras, D., Santurkar, S., Engstrom, L., Turner, A., and Madry, A · 2019
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Theoretically principled trade-off between robustness and accuracy
Zhang, H., Yu, Y., Jiao, J., Xing, E., Ghaoui, L. E., and Jordan, M · 2019
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Towards robust fine-grained recognition by maximal separation of discriminative features
Nakka, K. K. and Salzmann, M · 2020
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Fast is better than free: Revisiting adversarial training
Wong, E., Rice, L., and Kolter, J. Z · 2020
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Attribute prototype network for zero-shot learning
Xu, W., Xian, Y., Wang, J., Schiele, B., and Akata, Z · 2020
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