Online Continual Domain Adaptation for Semantic Image Segmentation Using
  Internal Representations
Serban Stan Mohammad Rostami
Semantic segmentation models trained on annotated data fail to generalize
well when the input data distribution changes over extended time period,
leading to requiring re-training to maintain performance. Classic Unsupervised
domain adaptation (UDA) attempts to address a similar problem when there is
target domain with no annotated data points through transferring knowledge from
a source domain with annotated data. We develop an online UDA algorithm for
semantic segmentation of images that improves model generalization on
unannotated domains in scenarios where source data access is restricted during
adaptation. We perform model adaptation is by minimizing the distributional
distance between the source latent features and the target features in a shared
embedding space. Our solution promotes a shared domain-agnostic latent feature
space between the two domains, which allows for classifier generalization on
the target dataset. To alleviate the need of access to source samples during
adaptation, we approximate the source latent feature distribution via an
appropriate surrogate distribution, in this case a Gassian mixture model (GMM).
We evaluate our approach on well established semantic segmentation datasets and
demonstrate it compares favorably against state-of-the-art (SOTA) UDA semantic
segmentation methods.