What is recorded here?
Averages sensitivity maps over Gaussian perturbations of an input to reduce visually noisy saliency, and studies visualization choices and training-time noise.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Visual coherence is evaluated on selected image models and does not certify causal faithfulness, completeness or robustness. Monte Carlo averaging approximates a smoothed map, not the exact unsmoothed gradient.
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People and systems
Daniel Smilkov; Nikhil Thorat; Been Kim; Fernanda Viégas; Martin Wattenberg
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
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English catalogue narrative
SmoothGrad: noise-averaged gradient sensitivity maps
Averages sensitivity maps over Gaussian perturbations of an input to reduce visually noisy saliency, and studies visualization choices and training-time noise.
Scientific scope
Visual coherence is evaluated on selected image models and does not certify causal faithfulness, completeness or robustness. Monte Carlo averaging approximates a smoothed map, not the exact unsmoothed gradient.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Daniel Smilkov; Nikhil Thorat; Been Kim; Fernanda Viégas; Martin Wattenberg
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.