Gated Domain Units for Multi-source Domain Generalization
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Distribution shift (DS) is a common problem that deteriorates the performance of learning machines. To overcome this problem, we postulate that real-world distributions are composed of elementary distributions that remain invariant across different domains. We call this an invariant elementary distribution (I.E.D.) assumption. This invariance thus enables knowledge transfer to unseen domains. To exploit this assumption in domain generalization (DG), we developed a modular neural network layer that consists of Gated Domain Units (GDUs). Each GDU learns an embedding of an individual elementary domain that allows us to encode the domain similarities during the training. During inference, the GDUs compute similarities between an observation and each of the corresponding elementary distributions which are then used to form a weighted ensemble of learning machines. Because our layer is trained with backpropagation, it can be easily integrated into existing deep learning frameworks. Our evaluation on Digits5, ECG, Camelyon17, iWildCam, and FMoW shows a significant improvement in the performance on out-of-training target domains without any access to data from the target domains. This finding supports the validity of the I.E.D. assumption in real-world data distributions.
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