  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.
