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Saliency methods are a popular class of feature attribution explanation methods that aim to capture a model's predictive reasoning by identifying "important" pixels in an input image.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Deep inside convolutional networks: Visualising image classification models and saliency maps, 2013
Simonyan, K., Vedaldi, A., and Zisserman, A · 2013
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
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Visualizing and understanding convolutional networks
Zeiler, M. D. 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.
The pascal visual object classes challenge: A retrospective
Everingham, M., Eslami, S. A., Van Gool, L., Williams, C. K., Winn, J., and Zisserman, A · 2015
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Striving for simplicity: The all convolutional net
Springenberg, J., Dosovitskiy, A., Brox, T., and Riedmiller, M · 2015
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Learning deep features for discriminative localization
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A · 2016
Earlier work this paper cites.
A unified approach to interpreting model predictions
Lundberg, S. M. and Lee, S.-I · 2017
Earlier work this paper cites.
Explaining nonlinear classification decisions with deep taylor decomposition
Montavon, G., Lapuschkin, S., Binder, A., Samek, W., and Müller, K.-R · 2017
Earlier work this paper cites.
Grad-cam: Visual explanations from deep networks via gradient-based localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D · 2017
Earlier work this paper cites.
Learning important features through propagating activation differences
Shrikumar, A., Greenside, P., and Kundaje, A · 2017
Earlier work this paper cites.
Smoothgrad: removing noise by adding noise
Smilkov, D., Thorat, N., Kim, B., Viégas, F., and Wattenberg, M · 2017
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Axiomatic attribution for deep networks
Sundararajan, M., Taly, A., and Yan, Q · 2017
Cited alongside, same era.
Counterfactual explanations without opening the black box: Automated decisions and the gdpr
Wachter, S., Mittelstadt, B., and Russell, C · 2017
Cited alongside, same era.
Places: A 10 million image database for scene recognition
Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., and Torralba, A · 2017
Cited alongside, same era.
Sanity checks for saliency maps
Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., and Kim, B · 2018
Cited alongside, same era.
Towards robust interpretability with self-explaining neural networks
Alvarez Melis, D. and Jaakkola, T · 2018
Cited alongside, same era.
Towards better understanding of gradient-based attribution methods for deep neural networks
Ancona, M., Ceolini, E., Öztireli, C., and Gross, M · 2018
A benchmark for interpretability methods in deep neural networks
Hooker, S., Erhan, D., Kindermans, P.-J., and Kim, B · 2019
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The (un) reliability of saliency methods
Kindermans, P.-J., Hooker, S., Adebayo, J., Alber, M., Schütt, K. T., Dähne, S., Erhan, D., and Kim, B · 2019
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Pseudo-lidar from visual depth estimation: Bridging the gap in 3d object detection for autonomous driving
Wang, Y., Chao, W.-L., Garg, D., Hariharan, B., Campbell, M., and Weinberger, K. Q · 2019
Later among the works it cites.
Benchmarking attribution methods with relative feature importance, 2019
Yang, M. and Kim, B · 2019
Later among the works it cites.
Debugging tests for model explanations
Adebayo, J., Muelly, M., Liccardi, I., and Kim, B · 2020
Later among the works it cites.
Assessing the (un) trustworthiness of saliency maps for localizing abnormalities in medical imaging
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Cited alongside, same era.
Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks
Chattopadhay, A., Sarkar, A., Howlader, P., and Balasubramanian, V. N · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
Cited alongside, same era.
Cbam: Convolutional block attention module
Woo, S., Park, J., Lee, J.-Y., and Kweon, I. S · 2018
Cited alongside, same era.
Top-down neural attention by excitation backprop
Zhang, J., Bargal, S. A., Lin, Z., Brandt, J., Shen, X., and Sclaroff, S · 2018
Cited alongside, same era.
innvestigate neural networks!
Alber, M., Lapuschkin, S., Seegerer, P., Hägele, M., Schütt, K. T., Montavon, G., Samek, W., Müller, K.-R., Dähne, S., and Kindermans, P.-J · 2019
Cited alongside, same era.
Res2net: A new multi-scale backbone architecture
Gao, S., Cheng, M.-M., Zhao, K., Zhang, X.-Y., Yang, M.-H., and Torr, P. H · 2019
Cited alongside, same era.
Arun, N., Gaw, N., Singh, P., Chang, K., Aggarwal, M., Chen, B., Hoebel, K., Gupta, S., Patel, J., Gidwani, M., et al · 2020
Later among the works it cites.
Sanity checks for saliency metrics
Tomsett, R., Harborne, D., Chakraborty, S., Gurram, P., and Preece, A · 2020
Later among the works it cites.
Score-cam: Score-weighted visual explanations for convolutional neural networks
Wang, H., Wang, Z., Du, M., Yang, F., Zhang, Z., Ding, S., Mardziel, P., and Hu, X · 2020
Later among the works it cites.
Synthetic benchmarks for scientific research in explainable machine learning
Liu, Y., Khandagale, S., White, C., and Neiswanger, W · 2021
Closest in time.
Do input gradients highlight discriminative features?
Shah, H., Jain, P., and Netrapalli, P · 2021
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Do users benefit from interpretable vision? a user study, baseline, and dataset
Sixt, L., Schuessler, M., Popescu, O.-I., Weiß, P., and Landgraf, T · 2021
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Do feature attribution methods correctly attribute features?
Zhou, Y., Booth, S., Ribeiro, M. T., and Shah, J · 2022
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