  Primordial B-mode detection is one of the main goals of next-generation
cosmic microwave background (CMB) experiments. Primordial B-modes are a unique
signature of primordial gravitational waves (PGWs). However, the gravitational
interaction of CMB photons with large-scale structures will distort the
primordial E modes, adding a lensing B-mode component to the primordial B-mode
signal. Removing the lensing effect (`delensing') from observed CMB
polarization maps will be necessary to improve the constraint of PGWs and
obtain a primordial E-mode signal. Here, we introduce a deep convolutional
neural network model named multi-input multi-output U-net (MIMO-UNet) to
perform CMB delensing. The networks are trained on simulated CMB maps with size
$20^{\circ} \times 20^{\circ}$. We first use MIMO-UNet to reconstruct the
unlensing CMB polarization ($Q$ and $U$) maps from observed CMB maps. The
recovered E-mode power spectrum exhibits excellent agreement with the
primordial EE power spectrum. The recovery of the primordial B-mode power
spectrum for noise levels of 0, 1, and 2 $\mu$K-arcmin is greater than 98\% at
the angular scale of $\ell<150$. We additionally reconstruct the lensing B map
from observed CMB maps. The recovery of the lensing B-mode power spectrum is
greater than roughly 99\% at the scales of $\ell>200$. We delens observed
B-mode power spectrum by subtracting reconstructed lensing B-mode spectrum. The
recovery of tensor B-mode power spectrum for noise levels of 0, 1, 2
$\mu$K-arcmin is greater than 98 \% at the angular scales of $\ell<120$. Even
at $\ell=160$, the recovery of tensor B-mode power spectrum is still around 71
\%.
