  We examine the discovery potential for double Higgs production at the high
luminosity LHC in the final state with two $b$-tagged jets, two leptons and
missing transverse momentum. Although this dilepton final state has been
considered a difficult channel due to the large backgrounds, we argue that it
is possible to obtain sizable signal significance, by adopting a deep learning
framework making full use of the relevant kinematics along with the jet images
from the Higgs decay. For the relevant number of signal events we obtain a
substantial increase in signal sensitivity over existing analyses. We discuss
relative improvements at each stage and the correlations among the different
input variables for the neutral network. The proposed method can be easily
generalized to the semi-leptonic channel of double Higgs production, as well as
to other processes with similar final states.
