Adaptive Risk Minimization: Learning to Adapt to Domain Shift (2007.02931v4)

Abstract: A fundamental assumption of most machine learning algorithms is that the training and test data are drawn from the same underlying distribution. However, this assumption is violated in almost all practical applications: machine learning systems are regularly tested under distribution shift, due to changing temporal correlations, atypical end users, or other factors. In this work, we consider the problem setting of domain generalization, where the training data are structured into domains and there may be multiple test time shifts, corresponding to new domains or domain distributions. Most prior methods aim to learn a single robust model or invariant feature space that performs well on all domains. In contrast, we aim to learn models that adapt at test time to domain shift using unlabeled test points. Our primary contribution is to introduce the framework of adaptive risk minimization (ARM), in which models are directly optimized for effective adaptation to shift by learning to adapt on the training domains. Compared to prior methods for robustness, invariance, and adaptation, ARM methods provide performance gains of 1-4% test accuracy on a number of image classification problems exhibiting domain shift.

• The paper introduces ARM, a novel meta-learning framework that enables real-time adaptation to domain shifts by leveraging unlabeled test data.
• The ARM framework features variants like ARM-CML, ARM-BN, and ARM-LL, each using unique strategies for context, normalization, and gradient-based adaptation.
• Experimental results on benchmarks such as Rotated MNIST and Wilds demonstrate up to 4% improvement in test accuracy over state-of-the-art methods.

Adaptive Risk Minimization: Learning to Adapt to Domain Shift

The paper "Adaptive Risk Minimization: Learning to Adapt to Domain Shift" addresses a critical challenge in machine learning: the distribution shift between training and test data. This issue arises due to the common assumption in empirical risk minimization (ERM) that the training and test data are drawn from the same distribution. In reality, this assumption is frequently violated, leading to deteriorated model performance when distribution shifts occur. The authors propose a novel approach, Adaptive Risk Minimization (ARM), aiming to optimize models directly for adaptation to domain shift by leveraging unlabeled test data.

Key Contributions and Methodology

The primary contribution of this work is the introduction of the ARM framework, which contrasts with prior methods focusing on robustness and invariance. While traditional approaches attempt to learn invariant feature spaces or robust models applicable across all domains, ARM instead focuses on learning models that can adapt at test time to shifting domains without requiring labeled test data.

ARM operates by using a meta-learning approach to train models that can adapt to new domains at test time by optimizing the models on training domains organized in a way that mimics possible test scenarios. This is achieved by structuring the training data into domains and optimizing the model so it can leverage unlabeled test examples.

The paper introduces several instantiations of the ARM framework:

• ARM-CML (Contextual Meta-Learning): This approach uses a context network that processes batches of inputs to produce a context representation, which in turn informs the prediction model.
• ARM-BN (Batch Normalization): Utilizes the test batch to compute normalization statistics used during test time adaptation. This simple yet effective method highlights the advantage of recalibrating batch normalization statistics with new data.
• ARM-LL (Learned Loss): A gradient-based meta-learning approach that updates model parameters based on a learned loss function, facilitating adaptation with unlabeled data.

Experimental Evaluation

The efficacy of ARM methods is demonstrated through various image classification tasks, including Rotated MNIST, FEMNIST, and image classification under corruption (CIFAR-10-C and Tiny ImageNet-C). The experiments show that ARM methods achieve notable gains in performance, achieving improvements of 1-4% in test accuracy over state-of-the-art methods.

ARM was also evaluated on the Wilds benchmark, which comprises real-world distribution shift problems. Results indicate that ARM-BN, in particular, substantially improves performance on certain tasks, such as RxRx1, highlighting its robustness in practical scenarios.

Implications and Future Directions

The ARM framework has significant implications for both practical applications and theoretical development in machine learning. Practically, ARM provides a mechanism to improve model robustness to domain shifts, which is crucial for deploying models in dynamic real-world environments. Theoretically, it challenges conventional paradigms by shifting focus from invariant feature learning to adaptability and contextual learning.

Future developments in AI could explore extending ARM to broader domains beyond image classification, including reinforcement learning and natural language processing. Moreover, developing strategies for gracefully incorporating adaptation when domain labels are not explicitly provided will be crucial for enhancing the ARM framework's applicability.

The ARM approach represents a promising direction in machine learning research, offering a pathway to more resilient and adaptable systems in the face of ever-changing data landscapes. The paper not only proposes a novel framework but also establishes a foundation for future research on learning adaptive models that naturally align with the pervasive phenomena of domain shifts.