Robustness to natural distribution shifts has seen remarkable progress thanks
to recent pre-training strategies combined with better fine-tuning methods.
However, such fine-tuning assumes access to large amounts of labelled data, and
the extent to which the observations hold when the amount of training data is
not as high remains unknown. We address this gap by performing the first
in-depth study of robustness to various natural distribution shifts in
different low-shot regimes: spanning datasets, architectures, pre-trained
initializations, and state-of-the-art robustness interventions. Most
importantly, we find that there is no single model of choice that is often more
robust than others, and existing interventions can fail to improve robustness
on some datasets even if they do so in the full-shot regime. We hope that our
work will motivate the community to focus on this problem of practical
importance.

本文研究了不同低样本情况下，自然分布偏移的鲁棒性，发现没有单一模型比其他模型更鲁棒，同时现有的干预手段在某些数据集上可能无法提高鲁棒性。

基准测试低数据量下对自然分布变化的鲁棒性

Benchmarking Low-Shot Robustness to Natural Distribution Shifts

Transfer learning for GANs successfully improves generation performance under
low-shot regimes. However, existing studies show that the pretrained model
using a single benchmark dataset is not generalized to various target datasets.
More importantly, the pretrained model can be vulnerable to copyright or
privacy risks as membership inference attack advances. To resolve both issues,
we propose an effective and unbiased data synthesizer, namely Primitives-PS,
inspired by the generic characteristics of natural images. Specifically, we
utilize 1) the generic statistics on the frequency magnitude spectrum, 2) the
elementary shape (i.e., image composition via elementary shapes) for
representing the structure information, and 3) the existence of saliency as
prior. Since our synthesizer only considers the generic properties of natural
images, the single model pretrained on our dataset can be consistently
transferred to various target datasets, and even outperforms the previous
methods pretrained with the natural images in terms of Fr'echet inception
distance. Extensive analysis, ablation study, and evaluations demonstrate that
each component of our data synthesizer is effective, and provide insights on
the desirable nature of the pretrained model for the transferability of GANs.

本文提出了一个基于自然图片通用特性，名为 Primitives-PS 的数据合成器，用于解决 GANs 转移学习时因典型数据集过拟合和会员推断攻击等问题。实验证明，我们的数据合成器各组件均有效，单一以我们数据集为预训练模型的 GANs 能够在各个目标数据集上表现出色，优于自然图像预先训练方法。