  Recent adversarial learning research has achieved very impressive progress
for modelling cross-domain data shifts in appearance space but its counterpart
in modelling cross-domain shifts in geometry space lags far behind. This paper
presents an innovative Geometry-Aware Domain Adaptation Network (GA-DAN) that
is capable of modelling cross-domain shifts concurrently in both geometry space
and appearance space and realistically converting images across domains with
very different characteristics. In the proposed GA-DAN, a novel multi-modal
spatial learning technique is designed which converts a source-domain image
into multiple images of different spatial views as in the target domain. A new
disentangled cycle-consistency loss is introduced which balances the cycle
consistency in appearance and geometry spaces and improves the learning of the
whole network greatly. The proposed GA-DAN has been evaluated for the classic
scene text detection and recognition tasks, and experiments show that the
domain-adapted images achieve superior scene text detection and recognition
performance while applied to network training.
