  Domain adaptation of visual detectors is a critical challenge, yet existing
methods have overlooked pixel appearance transformations, focusing instead on
bootstrapping and/or domain confusion losses. We propose a Semantic Pixel-Level
Adaptation Transform (SPLAT) approach to detector adaptation that efficiently
generates cross-domain image pairs. Our model uses aligned-pair and/or
pseudo-label losses to adapt an object detector to the target domain, and can
learn transformations with or without densely labeled data in the source (e.g.
semantic segmentation annotations). Without dense labels, as is the case when
only detection labels are available in the source, transformations are learned
using CycleGAN alignment. Otherwise, when dense labels are available we
introduce a more efficient cycle-free method, which exploits pixel-level
semantic labels to condition the training of the transformation network. The
end task is then trained using detection box labels from the source,
potentially including labels inferred on unlabeled source data. We show both
that pixel-level transforms outperform prior approaches to detector domain
adaptation, and that our cycle-free method outperforms prior models for
unconstrained cycle-based learning of generic transformations while running 3.8
times faster. Our combined model improves on prior detection baselines by 12.5
mAP adapting from Sim 10K to Cityscapes, recovering over 50% of the missing
performance between the unadapted baseline and the labeled-target upper bound.
