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SmoothGrad and VarGrad are techniques that enhance the empirical quality of standard saliency maps by adding noise to input.
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The effects of adding noise during backpropagation training on a generalization performance
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Operator inequalities related to cauchy-schwarz and hölder-mccarthy inequalities
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Adding noise to the input of a model trained with a regularized objective
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Imagenet large scale visual recognition challenge
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Evaluating the visualization of what a deep neural network has learned
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Selvaraju, Ramprasaath R, Cogswell, Michael, Das, Abhishek, Vedantam, Ramakrishna, Parikh, Devi, and Batra, Dhruv · 2017
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The (un) reliability of saliency methods
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Smoothgrad: removing noise by adding noise
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Local explanation methods for deep neural networks lack sensitivity to parameter values
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Towards better understanding of gradient-based attribution methods for deep neural networks
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Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks
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