MAP: Towards Balanced Generalization of IID and OOD through Model-Agnostic Adapters

Deep learning has achieved tremendous success in recent years, but most of these successes are built on an independent and identically distributed (IID) assumption. This somewhat hinders the application of deep learning to the more challenging out-of-distribution (OOD) scenarios. Although many OOD methods have been proposed to address this problem and have obtained good performance on testing data that is of major shifts with training distributions, interestingly, we experimentally find that these methods achieve excellent OOD performance by making a great sacrifice of the IID performance. We call this finding the IID-OOD dilemma. Clearly, in real-world applications, distribution shifts between training and testing data are often uncertain, where shifts could be minor, and even close to the IID scenario, and thus it is truly important to design a deep model with the balanced generalization ability between IID and OOD. To this end, in this paper, we investigate an intriguing problem of balancing IID and OOD generalizations and propose a novel Model Agnostic adaPters (MAP) method, which is more reliable and effective for distribution-shift-agnostic real-world data. Our key technical contribution is to use auxiliary adapter layers to incorporate the inductive bias of IID into OOD methods. To achieve this goal, we apply a bilevel optimization to explicitly model and optimize the coupling relationship between the OOD model and auxiliary adapter layers. We also theoretically give a first-order approximation to save computational time. Experimental results on six datasets successfully demonstrate that MAP can greatly improve the performance of IID while achieving good OOD performance.