Self-balanced Learning For Domain Generalization
This addresses performance degradation in domain generalization for real-world applications where training data has distribution gaps, though it is incremental as it builds on existing methods.
The paper tackles domain generalization with imbalanced multi-domain source data by proposing a self-balanced framework that adaptively learns loss weights using an auxiliary reweighting network and balanced meta data, achieving state-of-the-art performance.
Domain generalization aims to learn a prediction model on multi-domain source data such that the model can generalize to a target domain with unknown statistics. Most existing approaches have been developed under the assumption that the source data is well-balanced in terms of both domain and class. However, real-world training data collected with different composition biases often exhibits severe distribution gaps for domain and class, leading to substantial performance degradation. In this paper, we propose a self-balanced domain generalization framework that adaptively learns the weights of losses to alleviate the bias caused by different distributions of the multi-domain source data. The self-balanced scheme is based on an auxiliary reweighting network that iteratively updates the weight of loss conditioned on the domain and class information by leveraging balanced meta data. Experimental results demonstrate the effectiveness of our method overwhelming state-of-the-art works for domain generalization.