Training a generative adversarial network (GAN) with limited data has been a
challenging task. A feasible solution is to start with a GAN well-trained on a
large scale source domain and adapt it to the target domain with a few samples,
termed as few shot generative model adaption. However, existing methods are
prone to model overfitting and collapse in extremely few shot setting (less
than 10). To solve this problem, we propose a relaxed spatial structural
alignment method to calibrate the target generative models during the adaption.
We design a cross-domain spatial structural consistency loss comprising the
self-correlation and disturbance correlation consistency loss. It helps align
the spatial structural information between the synthesis image pairs of the
source and target domains. To relax the cross-domain alignment, we compress the
original latent space of generative models to a subspace. Image pairs generated
from the subspace are pulled closer. Qualitative and quantitative experiments
show that our method consistently surpasses the state-of-the-art methods in few
shot setting.

本论文提出了一种基于空间结构对少量数据进行对抗生成模型适配的方法，通过对源域和目标域的图像对进行跨域对齐，减轻目标生成模型过拟合和崩塌的问题，实验表明该方法在少样本学习中具有显著优势。

松弛空间结构对鲁棒性小样本生成模型适应的影响

Few Shot Generative Model Adaption via Relaxed Spatial Structural  Alignment

Unsupervised domain adaptation algorithms aim to transfer the knowledge
learned from one domain to another (e.g., synthetic to real images). The
adapted representations often do not capture pixel-level domain shifts that are
crucial for dense prediction tasks (e.g., semantic segmentation). In this
paper, we present a novel pixel-wise adversarial domain adaptation algorithm.
By leveraging image-to-image translation methods for data augmentation, our key
insight is that while the translated images between domains may differ in
styles, their predictions for the task should be consistent. We exploit this
property and introduce a cross-domain consistency loss that enforces our
adapted model to produce consistent predictions. Through extensive experimental
results, we show that our method compares favorably against the
state-of-the-art on a wide variety of unsupervised domain adaptation tasks.

本文提出了一种新颖的基于像素的对抗域自适应算法，通过利用图像到图像翻译方法进行数据增强，并引入跨域一致性损失来强制使得我们的自适应模型产生一致的预测，从而在广泛的无监督域自适应任务中获得与最先进方法相当的表现。