As with all synthetic data, there’s a shift from our domain to the one captured by real cameras. Although there’s no universal domain adaptation approach for every use-case, we stand on the shoulders of giants to get great results.
Adaptive Batch Normalization is a simple technique, can be easily applied to any network with batch normalization layers, and combined with all other techniques for surprisingly good results.

Adversarial domain adaptation and its modifications for particular tasks usually result in strong improvement. The downside is that it typically requires heavy pipeline modifications.

Image-2-image translation methods coupled with
self-regularization loss allows dataset-level refinement. While these methods require additional pipeline to train,
it is completely independent and does not require modifications of the main training pipeline.

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