Feature alignment is an approach to improving robustness to distribution shift that matches the distribution of feature activations between the training distribution and test distribution. A particularly simple but effective approach to feature alignment involves aligning the batch normalization statistics between the two distributions in a trained neural network. This technique has received renewed interest lately because of its impressive performance on robustness benchmarks. However, when and why this method works is not well understood. We investigate the approach in more detail and identify several limitations. We show that it only significantly helps with a narrow set of distribution shifts and we identify several settings in which it even degrades performance. We also explain why these limitations arise by pinpointing why this approach can be so effective in the first place. Our findings call into question the utility of this approach and Unsupervised Domain Adaptation more broadly for improving robustness in practice.

通过在训练神经网络时匹配测试集分布的特征激活分布来提高鲁棒性的特征对齐方法是一种简单有效的方法，但其局限性较为明显，只有在狭窄的分布转移情况下才会显著有所改善，并且有一些情况下它甚至会导致性能下降，因此本研究在更深层次探究了这种方法，疑问了该方法及更广泛的无监督域自适应方法对于提高实际鲁棒性的效用。

后验特征对鲁棒性的局限性