  We apply a novel model based on convolutional neural networks (CNNs) to
identify gravitationally-lensed galaxies in multi-band imaging of the Hyper
Suprime Cam Subaru Strategic Program (HSC-SSP) Survey. The trained model is
applied to a parent sample of 2 350 061 galaxies selected from the $\sim$ 800
deg$^2$ Wide area of the HSC-SSP Public Data Release 2. The galaxies in HSC
Wide are selected based on stringent pre-selection criteria, such as multiband
magnitudes, stellar mass, star formation rate, extendedness limit, photometric
redshift range, etc. Initially, the CNNs provide a total of 20 241 cutouts with
a score greater than 0.9, but this number is subsequently reduced to 1 522
cutouts by removing definite non-lenses for further inspection by human eyes.
We discover 43 definite and 269 probable lenses, of which 97 are completely
new. In addition, out of 880 potential lenses, we recovered 289 known systems
in the literature. We identify 143 candidates from the known systems that had
higher confidence in previous searches. Our model can also recover 285
candidate galaxy-scale lenses from the Survey of Gravitationally lensed Objects
in HSC Imaging (SuGOHI), where a single foreground galaxy acts as the
deflector. Even though group-scale and cluster-scale lens systems were not
included in the training, a sample of 32 SuGOHI-c (i.e., group/cluster-scale
systems) lens candidates was retrieved. Our discoveries will be useful for
ongoing and planned spectroscopic surveys, such as the Subaru Prime Focus
Spectrograph project, to measure lens and source redshifts in order to enable
detailed lens modelling.
