These tools and metrics are designed to help AI actors develop and use trustworthy AI systems and applications that respect human rights and are fair, transparent, explainable, robust, secure and safe.
Normalized Scanpath Saliency (NSS)
The Normalized Scanpath Saliency was introduced to the saliency community as a simple correspondence measure between saliency maps and ground truth, computed as the average normalized saliency at fixated locations. Unlike in AUC, the absolute saliency values are part of the normalization calculation. NSS is sensitive to false positives, relative differences in saliency across the image, and general monotonic transformations.
References
1. Nikulin, M.S., 2001. Hellinger distance. In Encyclopedia of Mathematics. EMS Press. 2. Hellinger, E., 1909. Neue Begründung der Theorie quadratischer Formen von unendlichvielen Veränderlichen. Journal für die reine und angewandte Mathematik, 136, pp.210–271. 3. Jeffreys, H., 1946. An invariant form for the prior probability in estimation problems. Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences, 186(1007), pp.453–461. 4. Liese, F. and Miescke, K.-J., 2008. Statistical Decision Theory: Estimation, Testing, and Selection. Springer. 5. Pardo, L., 2006. Statistical Inference Based on Divergence Measures. Chapman and Hall/CRC.
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