Example metrics for transfer learning. Left panel: distribution in arbitrary units of the network scores for the true accreted (black) and in situ (blue) test stars of m12f LSR0. We note that the stars that are truly in situ have network scores peaked towards 0, while those that are truly accreted have scores peaked towards 1. The orange and green arrows indicate two cuts on the network scores: S >  0.50 (orange) and S >  0.75 (green). For the example illustrated here, the scores are specific to a network where transfer learning was performed on the last layer using the ZM selection to derive labels. In the remaining panels, the orange and green lines show the normalized v_R, v_ϕ, and v_z distributions of the stars with scores larger than the indicated cut. The thick black lines correspond to the distribution of the truth-level accreted stars, not just the ones passing the cuts. We see that cutting on a network score of 0.75 (green lines) better reproduces the truth distributions. To quantify the goodness-of-fit, we calculate the χ² for the v_R, v_ϕ, and v_z distributions separately, and sum them together to get a total ∑χ². The lower the value of ∑χ², the better the network reproduces the truth distributions. For the case illustrated here, the ∑χ² is a factor of 3 smaller for the green distributions, compared to the orange ones.