$$\mathrm{RRR}(\pi) = \frac{\mathrm{CAI}(\pi)}{s_{\mathrm{patio}}(\pi)}$$

Equation 7 · AI Research

Every Test Changed the Scene and Kept the Chair Red

This equation states an equality: the expressions on both sides have the same value under the article’s assumptions.

Pair it with a Recolor Robustness Ratio , comparing that score against the architecture’s own already-published performance on the paper’s hardest matched-appearance site: $\mathrm{RRR}(\pi) = \frac{\mathrm{CAI}(\pi)}{s_{\mathrm{patio}}(\pi)}$. where $s_{\mathrm{patio}}(\pi)$ is the architecture’s own Table 1 Urban Patio success rate [ 1 ] . An $\mathrm{RRR}(\pi)$ near 1 says the architecture tracks the target about as well when its color is no longer the give-away cue as when it was — the causal reading survives. An $\mathrm{RRR}(\pi)$ collapsing toward 0 says performance was riding on the color the whole time, and collapses precisely when that color is taken away and handed to a decoy — the shift-stable-correlate account is confirmed instead.

Meanings in this article

• $s_{\mathrm{patio}}$: the architecture’s own Table 1 Urban Patio success rate [ 1 ].

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