  In the context of large spectroscopic surveys of stars, data-driven methods
are key in deducing physical parameters for millions of spectra in a short
time. Convolutional neural networks (CNNs) enable us to connect observables
(e.g. spectra, stellar magnitudes) to physical properties (atmospheric
parameters, chemical abundances, or labels in general). We trained a CNN,
adopting stellar atmospheric parameters and chemical abundances from APOGEE
DR16 (resolution R=22500) data as training set labels. As input, we used parts
of the intermediate-resolution RAVE DR6 spectra (R~7500) overlapping with the
APOGEE DR16 data as well as broad-band ALL_WISE and 2MASS photometry, together
with Gaia DR2 photometry and parallaxes. We derived precise atmospheric
parameters Teff, log(g), and [M/H] along with the chemical abundances of
[Fe/H], [alpha/M], [Mg/Fe], [Si/Fe], [Al/Fe], and [Ni/Fe] for 420165 RAVE
spectra. The precision typically amounts to 60K in Teff, 0.06 in log(g) and
0.02-0.04 dex for individual chemical abundances. Incorporating photometry and
astrometry as additional constraints substantially improves the results in
terms of the accuracy and precision of the derived labels. We provide a
catalogue of CNN-trained atmospheric parameters and abundances along with their
uncertainties for 420165 stars in the RAVE survey. CNN-based methods provide a
powerful way to combine spectroscopic, photometric, and astrometric data
without the need to apply any priors in the form of stellar evolutionary
models. The developed procedure can extend the scientific output of RAVE
spectra beyond DR6 to ongoing and planned surveys such as Gaia RVS, 4MOST, and
WEAVE. We call on the community to place a particular collective emphasis and
on efforts to create unbiased training samples for such future spectroscopic
surveys.
