  Baikal-GVD is a large ($\sim$1 km$^3$) underwater neutrino telescope
installed in the fresh waters of Lake Baikal. The deep lake water environment
is pervaded by background light, which is detectable by Baikal-GVD's
photosensors. We introduce a neural network for an efficient separation of
these noise hits from the signal ones, stemming from the propagation of
relativistic particles through the detector. The model has a U-net-like
architecture and employs temporal (causal) structure of events. The neural
network's metrics reach up to 99\% signal purity (precision) and 96\% survival
efficiency (recall) on Monte-Carlo simulated dataset. We compare the developed
method with the algorithmic approach to rejecting the noise and discuss other
possible architectures of neural networks, including graph-based ones.
