Machine learning models typically suffer from the domain shift problem when
trained on a source dataset and evaluated on a target dataset of different
distribution. To overcome this problem, domain generalisation (DG) methods aim
to leverage data from multiple source domains so that a trained model can
generalise to unseen domains. In this paper, we propose a novel DG approach
based on \emph{Deep Domain-Adversarial Image Generation} (DDAIG). Specifically,
DDAIG consists of three components, namely a label classifier, a domain
classifier and a domain transformation network (DoTNet). The goal for DoTNet is
to map the source training data to unseen domains. This is achieved by having a
learning objective formulated to ensure that the generated data can be
correctly classified by the label classifier while fooling the domain
classifier. By augmenting the source training data with the generated unseen
domain data, we can make the label classifier more robust to unknown domain
changes. Extensive experiments on four DG datasets demonstrate the
effectiveness of our approach.

本文介绍了一种基于深度生成对抗网络的领域泛化方法，通过生成新的数据使学习模型适应未知的测试领域，从而提高模型的准确性和鲁棒性。

领域对抗深度图像生成用于领域泛化

Deep Domain-Adversarial Image Generation for Domain Generalisation

We present LR-GAN: an adversarial image generation model which takes scene
structure and context into account. Unlike previous generative adversarial
networks (GANs), the proposed GAN learns to generate image background and
foregrounds separately and recursively, and stitch the foregrounds on the
background in a contextually relevant manner to produce a complete natural
image. For each foreground, the model learns to generate its appearance, shape
and pose. The whole model is unsupervised, and is trained in an end-to-end
manner with gradient descent methods. The experiments demonstrate that LR-GAN
can generate more natural images with objects that are more human recognizable
than DCGAN.

我们提出了 LR-GAN，这是一种考虑场景结构和上下文的对抗性图像生成模型，使用前景和背景分别和递归地生成，并以相关上下文方式将前景缝合在背景上，以产生完整自然图像。