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 towards 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, with the goal of minimizing the task error subject to 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 as well as classification and object pose estimation on the Cropped LineMOD dataset and compare to a number of domain adaptation approaches, demonstrating similar results with superior generalization capabilities.

本文提出了一种新的方法来解决合成和真实数据之间的领域适应问题，该方法利用任务网络作为其自己的对抗指南来找到最大化输出不确定性的有用增强，并设计了一个极小极大优化方案来最小化任务误差，针对欺骗者强制执行的特定约束条件，迷惑网络从可微分的像素级扰动中进行采样并利用任务体系结构找到最具破坏性的增强，从而实现了源数据到多个目标分布的稳健映射。

DeceptionNet:面向网络的领域随机化