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Integrated Gradients has become a popular method for post-hoc model interpretability.
Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, K., Vedaldi, A., and Zisserman, A · 2014
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Visualizing and understanding convolutional networks
Zeiler, M. D. and Fergus, R · 2014
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Striving for simplicity: The all convolutional net
Springenberg, J. T., Dosovitskiy, A., Brox, T., and Riedmiller, M · 2015
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A taxonomy and library for visualizing learned features in convolutional neural networks
Grün, F., Rupprecht, C., Navab, N., and Tombari, F · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Evaluating the visualization of what a deep neural network has learned
Samek, W., Binder, A., Montavon, G., Lapuschkin, S., and Müller, K.-R · 2016
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Sundararajan, M., Taly, A., and Yan, Q · 2016
Cited alongside, same era.
Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
Cited alongside, same era.
A unified approach to interpreting model predictions
Lundberg, S. M. and Lee, S.-I · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Learning important features through propagating activation differences
Shrikumar, A., Greenside, P., and Kundaje, A · 2017
Cited alongside, same era.
Axiomatic attribution for deep networks
Sundararajan, M., Taly, A., and Yan, Q · 2017
How important is a neuron?
Dhamdhere, K., Sundararajan, M., and Yan, Q · 2019
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XRAI: Better attributions through regions
Kapishnikov, A., Bolukbasi, T., Viégas, F., and Terry, M · 2019
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PyTorch Captum, 2019
Kokhlikyan, N., Miglani, V., Martin, M., Wang, E., Reynolds, J., Melnikov, A., Lunova, N., and Reblitz-Richardson, O · 2019
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Generalized integrated gradients: A practical method for explaining diverse ensembles
Merrill, J., Ward, G., Kamkar, S., Budzik, J., and Merrill, D · 2019
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On the (in) fidelity and sensitivity of explanations
Yeh, C.-K., Hsieh, C.-Y., Suggala, A., Inouye, D. I., and Ravikumar, P. K · 2019
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Visualizing the impact of feature attribution baselines
Sturmfels, P., Lundberg, S., and Lee, S.-I · 2020
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Cited alongside, same era.
Rise: Randomized input sampling for explanation of black-box models
Petsiuk, V., Das, A., and Saenko, K · 2018
Cited alongside, same era.
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