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A particular class of Explainable AI (XAI) methods provide saliency maps to highlight part of the image a Convolutional Neural Network (CNN) model looks at to classify the image as a way to explain its working.
The Caltech-UCSD Birds-200-2011 Dataset
Wah, C., Branson, S., Welinder, P., Perona, P., and Belongie, S · 2011
Earlier work this paper cites.
Analyzing likert data
Boone, H. N. and Boone, D. A · 2012
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, K., Vedaldi, A., and Zisserman, A · 2013
Earlier work this paper cites.
Analyzing and interpreting data from likert-type scales
Sullivan, G. M. and Artino Jr, A. R · 2013
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
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.
A unified approach to interpreting model predictions
Lundberg, S. and Lee, S.-I · 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
Cited alongside, same era.
Learning important features through propagating activation differences
Shrikumar, A., Greenside, P., and Kundaje, A · 2017
Cited alongside, same era.
Smoothgrad: removing noise by adding noise
Smilkov, D., Thorat, N., Kim, B., Viégas, F., and Wattenberg, M · 2017
Cited alongside, same era.
Axiomatic attribution for deep networks
Sundararajan, M., Taly, A., and Yan, Q · 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.
Do semantic parts emerge in convolutional neural networks?
Top-down neural attention by excitation backprop
Zhang, J., Bargal, S. A., Lin, Z., Brandt, J., Shen, X., and Sclaroff, S · 2018
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Full-gradient representation for neural network visualization
Srinivas, S. and Fleuret, F · 2019
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Evaluating saliency map explanations for convolutional neural networks: a user study
Alqaraawi, A., Schuessler, M., Weiß, P., Costanza, E., and Berthouze, N · 2020
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Quantitative evaluations on saliency methods: An experimental study
Li, X.-H., Shi, Y., Li, H., Bai, W., Song, Y., Cao, C. C., and Chen, L · 2020
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When explanations lie: Why many modified bp attributions fail
Sixt, L., Granz, M., and Landgraf, T · 2020
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Gonzalez-Garcia, A., Modolo, D., and Ferrari, V · 2018
Cited alongside, same era.
Zero-shot learning—a comprehensive evaluation of the good, the bad and the ugly
Xian, Y., Lampert, C. H., Schiele, B., and Akata, Z · 2018
Cited alongside, same era.
Tjoa, E. and Guan, C · 2020
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