🔬

Sanity Checks for Saliency Maps

📅 2018
technical research breakthrough
🔵 Rare

📖 Description

Sanity Checks for Saliency Maps Julius Adebayo , Justin Gilmer], Michael Muelly], Ian Goodfellow], Moritz Hardt]y, Been Kim] [email address redacted] ,[email address redacted] ]Google Brain yUniversity of California Berkeley Abstract Saliency methods have emerged as a popular tool to highlight features in an input deemed relevant for the prediction of a learned model. Several saliency methods have been proposed, often guided by visual appeal on image data. In this work, we propose an actionable methodology to evaluate what kinds of explanations a given method can and cannot provide. We ?nd that reliance, solely, on visual assessment can be misleading. Through extensive experiments we show that some existing saliency methods are independent both of the model and of the data generating process. Consequently, methods that fail the proposed tests are inadequate for tasks that are sensitive to either data or model, such as, ?nding outliers in the data, explaining the re...

📊 Game Impacts

Not verified in game

Which variables this event was proposed to move, and in which direction. The magnitudes are held in the corpus but are not shown here, because they have not been verified against the shipped game. They come from pdoom-data. They describe what an event was proposed to do, not what the shipped game does with it. Most events in the corpus are flavour: they are shown for colour and do not move any game variable. Only a small minority reach the systems below, and several of the variables listed here are not read by the game at all yet. Treat this table as a design proposal under review, not as a measurement of play. Corrections and arguments are welcome — the suggestion links at the foot of this page go straight to the data repo.

Variable Direction Condition
Research proposed: up Always
Papers proposed: up Always
Vibey Doom proposed: up Always

💭 Reactions

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🔗 Sources

🏷️ Event Metadata

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