  Similar to light, gravitational waves (GWs) can be lensed. Such lensing
phenomena can magnify the waves, create multiple images observable as repeated
events, and superpose several waveforms together, inducing potentially
discernible patterns on the waves. In particular, when the lens is small,
$\lesssim 10^5 M_\odot$, it can produce lensed images with time delays shorter
than the typical gravitational-wave signal length that conspire together to
form ``beating patterns''. We present a proof-of-principle study utilizing deep
learning for identification of such a lensing signature. We bring the
excellence of state-of-the-art deep learning models at recognizing foreground
objects from background noises to identifying lensed GWs from noise present
spectrograms. We assume the lens mass is around $10^3 M_\odot$ -- $10^5
M_\odot$, which can produce the order of millisecond time delays between two
images of lensed GWs. We discuss the feasibility of distinguishing lensed GWs
from unlensed ones and estimating physical and lensing parameters. Suggested
method may be of interest to the study of more complicated lensing
configurations for which we do not have accurate waveform templates.
