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Style-transfer GANs for bridging the domain gap in synthetic pose estimator training
[article]
2020
arXiv
pre-print
Given the dependency of current CNN architectures on a large training set, the possibility of using synthetic data is alluring as it allows generating a virtually infinite amount of labeled training data. However, producing such data is a non-trivial task as current CNN architectures are sensitive to the domain gap between real and synthetic data. We propose to adopt general-purpose GAN models for pixel-level image translation, allowing to formulate the domain gap itself as a learning problem.
arXiv:2004.13681v2
fatcat:4mfmp22l35fwtbhuhpudvm5bsu