  In this paper, we tackle the problem of training with multiple source domains
with the aim to generalize to new domains at test time without an adaptation
step. This is known as domain generalization (DG). Previous works on DG assume
identical categories or label space across the source domains. In the case of
category shift among the source domains, previous methods on DG are vulnerable
to negative transfer due to the large mismatch among label spaces, decreasing
the target classification accuracy. To tackle the aforementioned problem, we
introduce an end-to-end feature-norm network (FNN) which is robust to negative
transfer as it does not need to match the feature distribution among the source
domains. We also introduce a collaborative feature-norm network (CFNN) to
further improve the generalization capability of FNN. The CFNN matches the
predictions of the next most likely categories for each training sample which
increases each network's posterior entropy. We apply the proposed FNN and CFNN
networks to the problem of DG for image classification tasks and demonstrate
significant improvement over the state-of-the-art.
