  This paper focuses on the domain generalization task where domain knowledge
is unavailable, and even worse, only samples from a single domain can be
utilized during training. Our motivation originates from the recent progresses
in deep neural network (DNN) testing, which has shown that maximizing neuron
coverage of DNN can help to explore possible defects of DNN (i.e.,
misclassification). More specifically, by treating the DNN as a program and
each neuron as a functional point of the code, during the network training we
aim to improve the generalization capability by maximizing the neuron coverage
of DNN with the gradient similarity regularization between the original and
augmented samples. As such, the decision behavior of the DNN is optimized,
avoiding the arbitrary neurons that are deleterious for the unseen samples, and
leading to the trained DNN that can be better generalized to
out-of-distribution samples. Extensive studies on various domain generalization
tasks based on both single and multiple domain(s) setting demonstrate the
effectiveness of our proposed approach compared with state-of-the-art baseline
methods. We also analyze our method by conducting visualization based on
network dissection. The results further provide useful evidence on the
rationality and effectiveness of our approach.
