A large pool of diverse artistic styles for style-transfer augmentation improves domain generalization in driving vision models more than repeated use of few styles or domain-matched styles, yielding the lightweight StyleMixDG method with gains on GTAV-to-real benchmarks.
In: ICRA (2023)
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Evaluation of Randomization through Style Transfer for Enhanced Domain Generalization
A large pool of diverse artistic styles for style-transfer augmentation improves domain generalization in driving vision models more than repeated use of few styles or domain-matched styles, yielding the lightweight StyleMixDG method with gains on GTAV-to-real benchmarks.