  We use deep learning to explore the nature of observed giant clumps in
high-redshift disc galaxies, based on their identification and classification
in cosmological simulations. Simulated clumps are detected using the 3D gas and
stellar densities in the VELA zoom-in cosmological simulation suite, with $\sim
\!\! 25\ \!\rm{pc}$ maximum resolution, targeting main sequence galaxies at
$1\!<\!z\!<\!3$. The clumps are classified as long-lived clumps (LLCs) or
short-lived clumps (SLCs) based on their longevity in the simulations. We then
train neural networks to detect and classify the simulated clumps in mock,
multi-color, dusty and noisy HST-like images. The clumps are detected using an
encoder-decoder convolutional neural network (CNN), and are classified
according to their longevity using a vanilla CNN. Tests using the simulations
show our detector and classifier to be $\sim80\%$ complete and $\sim80\%$ pure
for clumps more massive than $\sim10^{7.5}\rm{M_\odot}$. When applied to
observed galaxies in the CANDELS/GOODS S+N fields, we find both types of clumps
to appear in similar abundances in the simulations and the observations. LLCs
are, on average, more massive than SLCs by $\sim 0.5\ \rm{dex}$, and they
dominate the clump population above $M_{\rm c}\gtrsim 10^{7.6}\ \rm{M_\odot}$.
LLCs tend to be found closer to the galactic centre, indicating clump migration
to the centre or preferential formation at smaller radii. The LLCs are found to
reside in high mass galaxies, indicating better clump survivability under
supernova feedback there, due to clumps being more massive in these galaxies.
We find the clump properties in the simulations and the observations to agree
within a factor of $\sim\! 2$.
