  Open compound domain adaptation (OCDA) has emerged as a practical adaptation
setting which considers a single labeled source domain against a compound of
multi-modal unlabeled target data in order to generalize better on novel unseen
domains. We hypothesize that an improved disentanglement of domain-related and
task-related factors of dense intermediate layer features can greatly aid OCDA.
Prior-arts attempt this indirectly by employing adversarial domain
discriminators on the spatial CNN output. However, we find that latent features
derived from the Fourier-based amplitude spectrum of deep CNN features hold a
more tractable mapping with domain discrimination. Motivated by this, we
propose a novel feature space Amplitude Spectrum Transformation (AST). During
adaptation, we employ the AST auto-encoder for two purposes. First, carefully
mined source-target instance pairs undergo a simulation of cross-domain feature
stylization (AST-Sim) at a particular layer by altering the AST-latent. Second,
AST operating at a later layer is tasked to normalize (AST-Norm) the domain
content by fixing its latent to a mean prototype. Our simplified adaptation
technique is not only clustering-free but also free from complex adversarial
alignment. We achieve leading performance against the prior arts on the OCDA
scene segmentation benchmarks.
