Real world uses of deep learning require predictable model behavior under
distribution shifts. Models such as CLIP show emergent natural distributional
robustness comparable to humans, but may require hundreds of millions of
training samples. Can we train robust learners in a domain where data is
limited? To rigorously address this question, we introduce JANuS (Joint
Annotations and Names Set), a collection of four new training datasets with
images, labels, and corresponding captions, and perform a series of carefully
controlled investigations of factors contributing to robustness in image
classification, then compare those results to findings derived from a
large-scale meta-analysis. Using this approach, we show that standard ResNet-50
trained with the cross-entropy loss on 2.4 million image samples can attain
comparable robustness to a CLIP ResNet-50 trained on 400 million samples. To
our knowledge, this is the first result showing (near) state-of-the-art
distributional robustness on limited data budgets. Our dataset is available at
\url{this https URL}, and the code used
to reproduce our experiments can be found at
\url{this https URL}.

在数据有限的情况下，我们通过在图像分类中使用损失函数为交叉熵的标准 ResNet-50 模型在 240 万图像样本上进行训练，展示出与训练了 4 亿个样本的 CLIP ResNet-50 模型相媲美的分布鲁棒性的结果。这是我们所知的首个在有限数据预算上展示出（接近）当前最先进的分布鲁棒性的研究成果。