OpenPAE benchmark demonstrates that few-shot personalization using same-person references consistently improves face age estimation accuracy beyond global models or simple domain adaptation.
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Deep learning infers Δν and ν_max from one-month TESS and K2 observations of red giants with reliable results for ~50% of Kepler/K2 samples and ~23% of TESS stars, plus ΔΠ1 for ~200 K2 young red giants that match known patterns.
AMRL reaches state-of-the-art accuracy on apparent age estimation yet exhibits clear performance drops for Asian and African American groups due to inconsistent feature focus, showing that technical tweaks are not enough without diverse localized data.
citing papers explorer
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Few-Shot Personalized Age Estimation
OpenPAE benchmark demonstrates that few-shot personalization using same-person references consistently improves face age estimation accuracy beyond global models or simple domain adaptation.
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Inferring Asteroseismic Parameters from Short Observations Using Deep Learning: Application to TESS and K2 Red Giants
Deep learning infers Δν and ν_max from one-month TESS and K2 observations of red giants with reliable results for ~50% of Kepler/K2 samples and ~23% of TESS stars, plus ΔΠ1 for ~200 K2 young red giants that match known patterns.
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Apparent Age Estimation: Challenges and Outcomes
AMRL reaches state-of-the-art accuracy on apparent age estimation yet exhibits clear performance drops for Asian and African American groups due to inconsistent feature focus, showing that technical tweaks are not enough without diverse localized data.