Towards Context-Aware Domain Generalization: Understanding the Benefits and Limits of Marginal Transfer Learning

TL;DR

The paper addresses how context about an input can improve predictions across unseen domains by formalizing context as a permutation-invariant set representation derived from data from the same domain. It develops necessary criteria for when context can help, analyzes robustness to distribution shifts, and introduces a set-encoder-based approach to capture contextual information, enabling environment-conditioned predictions. Empirical results on synthetic and ProDAS datasets illustrate scenarios where context improves performance and where it enables reliable out-of-distribution detection, facilitating a principled trade-off between predictive accuracy and robustness. The work offers theoretical insights and practical mechanisms for selecting between predictive and robust models in domain generalization, with implications for safer and more reliable cross-domain learning.

Abstract

In this work, we analyze the conditions under which information about the context of an input $X$ can improve the predictions of deep learning models in new domains. Following work in marginal transfer learning in Domain Generalization (DG), we formalize the notion of context as a permutation-invariant representation of a set of data points that originate from the same domain as the input itself. We offer a theoretical analysis of the conditions under which this approach can, in principle, yield benefits, and formulate two necessary criteria that can be easily verified in practice. Additionally, we contribute insights into the kind of distribution shifts for which the marginal transfer learning approach promises robustness. Empirical analysis shows that our criteria are effective in discerning both favorable and unfavorable scenarios. Finally, we demonstrate that we can reliably detect scenarios where a model is tasked with unwarranted extrapolation in out-of-distribution (OOD) domains, identifying potential failure cases. Consequently, we showcase a method to select between the most predictive and the most robust model, circumventing the well-known trade-off between predictive performance and robustness.

Figures (7)

• Figure 5: Experiment 2: Relative improvement of set-encoder (shown in I) approach versus baseline model (0 means, no improvement is achieved) on ProDAS dataset. We also show I (OOD) on OOD data. II depicts the relative improvement of the environment-oracle model compared to the baseline model. III demonstrates the relative improvement in predicting the environment when using contextual information compared to the absence of it. Variations arise from using different seeds to partition the ID data into training, test and validation set.
• Figure 6: Illustration of \ref{['prop:main']}. The first row depicts (a), the second row (b) and the third row (c). The pink framed plots show the conditional distributions along the pink marker as shown on the right.
• Figure 7: Experiment 1. Predictions performed on the toy dataset illustrated in \ref{['fig:simpson_example']}. We show predictions made by both our set-encoder approach and the vanilla model in the ID and OOD settings.
• Figure 8: Experiment 1. Verification of criteria. In I we depict the relative improvement of our approach versus a baseline model. We also show I (OOD) on OOD data. In II we show the relative improvement of the oracle model compared to the baseline. In III we compare the relative improvement of the contextual environment model with respect to the baseline environment model.
• Figure 9: Experiment 1. Models are trained on all environments except the OOD environment. "Extrapolation", i.e. when environment 1 or 5 is OOD, is a particularly hard task in this setting. The set-based model shows slightly better extrapolation capabilities. Generally, our model exhibits adaptability to diverse environments, addressing a limitation present in the baseline model.

Theorems & Definitions (2)

• Theorem C.1
• proof