  Upcoming large-area narrow band photometric surveys, such as J-PAS, will
enable us to observe a large number of galaxies simultaneously and efficiently.
However, it will be challenging to analyse the spatially-resolved stellar
populations of galaxies from such big data to investigate galaxy formation and
evolutionary history. We have applied a convolutional neural network (CNN)
technique, which is known to be computationally inexpensive once it is trained,
to retrieve the metallicity and age from J-PAS-like narrow band images. The CNN
was trained using mock J-PAS data created from the CALIFA IFU survey and the
age and metallicity at each data point, which are derived using full spectral
fitting to the CALIFA spectra. We demonstrate that our CNN model can
consistently recover age and metallicity from each J-PAS-like spectral energy
distribution. The radial gradients of the age and metallicity for galaxies are
also recovered accurately, irrespective of their morphology. However, it is
demonstrated that the diversity of the dataset used to train the neural
networks has a dramatic effect on the recovery of galactic stellar population
parameters. Hence, future applications of CNNs to constrain stellar populations
will rely on the availability of quality spectroscopic data from samples
covering a wide range of population parameters.
