A common explanation for the failure of deep networks to generalize out-of-distribution is that they fail to recover the "correct" features. Focusing on the domain generalization setting, we challenge this notion with a simple experiment which suggests that ERM already learns sufficient features and that the current bottleneck is not feature learning, but robust regression. We therefore argue that devising simpler methods for learning predictors on existing features is a promising direction for future research. Towards this end, we introduce Domain-Adjusted Regression (DARE), a convex objective for learning a linear predictor that is provably robust under a new model of distribution shift. Rather than learning one function, DARE performs a domain-specific adjustment to unify the domains in a canonical latent space and learns to predict in this space. Under a natural model, we prove that the DARE solution is the minimax-optimal predictor for a constrained set of test distributions. Further, we provide the first finite-environment convergence guarantee to the minimax risk, improving over existing results which show a "threshold effect". Evaluated on finetuned features, we find that DARE compares favorably to prior methods, consistently achieving equal or better performance.

本研究通过实验挑战了深度网络泛化不良的原因在于恢复“正确”特征失败的观点，并提出了一种新的领域调整回归（DARE）方法，该方法根据分布转移的新模型，通过域特定调整来统一一个规范潜在空间，并在该空间中学习预测。在有限环境下，该方法的收敛保证了最小最优预测器，相比之前的方法，该方法表现更好。

领域调整回归：ERM可能已经学会了足以满足领域外推广的特征