DeceptionNet: Network-Driven Domain Randomization.

Sergey Zakharov, Wadim Kehl, Slobodan Ilic

Abstract:

We present a novel approach to tackle domain adaptation between synthetic and real data. Instead, of employing ”blind” domain randomization, i.e., augmenting synthetic renderings with random backgrounds or changing illumination and colorization, we leverage the task network
as its own adversarial guide toward useful augmentations
that maximize the uncertainty of the output. To this end, we
design a min-max optimization scheme where a given task
competes against a special deception network to minimize
the task error subject to the specific constraints enforced by
the deceiver. The deception network samples from a family
of differentiable pixel-level perturbations and exploits the
task architecture to find the most destructive augmentations.
Unlike GAN-based approaches that require unlabeled data
from the target domain, our method achieves robust mappings that scale well to multiple target distributions from
source data alone. We apply our framework to the tasks of
digit recognition on enhanced MNIST variants, classification and object pose estimation on the Cropped LineMOD
dataset as well as semantic segmentation on the Cityscapes
dataset and compare it to a number of domain adaptation
approaches, thereby demonstrating similar results with superior generalization capabilities.