A Language Anchor-Guided Method for Robust Noisy Domain Generalization

Abstract--Real-world machine learning applications are often hindered by two critical challenges: distribution shift and label noise. Networks inherently tend to overfit to redundant, uninformative features present in the training distribution, which undermines their ability to generalize effectively to the target domain's distribution. The presence of noisy data further exacerbates this issue by inducing additional overfitting to noise, causing existing domain generalization methods to fail in effectively distinguishing invariant features from spurious ones. We also introduce a weighted loss function that dynamically adjusts the contribution of each sample based on its distance to the corresponding NLP anchor, thereby improving the model's resilience to noisy labels. Generalization (DG) has emerged as a pivotal algorithm in machine learning, aiming to develop models that can maintain high performance on previously unseen environments--or domains. T raditional methods often assume that training and test data share the same distribution, yet in real-world scenarios, there is frequently a substantial shift between these distributions. This phenomenon, widely referred to as domain shift, can cause severe performance degradation in tasks spanning computer vision, natural language processing, and medical image analysis [1]. As shown in Figure 1(a)(b), even within the same class label, the distribution of feature representations can vary considerably . This variation may stem from differences in image acquisition conditions--such as lighting variations, changes in pose, or complex background environments--and even from more subtle domain-specific factors like sensor noise or camera calibration differences. Such intra-class variability poses a significant challenge for developing accurate and adaptable models, which must learn to extract invariant features that capture the true semantic essence of the class while ignoring irrelevant variations. Lin, Z. Zhang is with Worcester Polytechnic Institute, Worcester, MA, 01890. L.Wang is with Carnegie Mellon University, Pittsburgh, P A, 15213. Y .Wang is with Peking University, Beijing, China, 100871. Z.Li, W.Lu is with T singhua University, Beijing, China, 100190. K.Y amada is with T ohoku University, Sendai, Japan, 980-8572.