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

In some scenarios, classifier requires detecting out-of-distribution samples
far from its training data. With desirable characteristics, reconstruction
autoencoder-based methods deal with this problem by using input reconstruction
error as a metric of novelty vs. normality. We formulate the essence of such
approach as a quadruplet domain translation with an intrinsic bias to only
query for a proxy of conditional data uncertainty. Accordingly, an improvement
direction is formalized as maximumly compressing the autoencoder's latent space
while ensuring its reconstructive power for acting as a described domain
translator. From it, strategies are introduced including semantic
reconstruction, data certainty decomposition and normalized L2 distance to
substantially improve original methods, which together establish
state-of-the-art performance on various benchmarks, e.g., the FPR@95%TPR of
CIFAR-100 vs. TinyImagenet-crop on Wide-ResNet is 0.2%. Importantly, our method
works without any additional data, hard-to-implement structure, time-consuming
pipeline, and even harming the classification accuracy of known classes.

使用基于重构自编码器的方法处理离群样本检测问题，通过最大化压缩自编码器的潜在空间、语义重构、数据置信度分解和归一化 L2 距离等策略有效地提高方法性能，达到了各类基准测试的最新技术水平。