… a set of source domains a model that is generalizable to any unseen domain. In this paper,
… domains. Our method, termed MixStyle, is motivated by the observation that visual domain is …

… Neural networks do not generalize well to unseen data with domain shifts—a … propose
MixStyle, a simple plug-and-play, parameter-free module that can improve domain generalization …

… to never-before-seen target domains. We propose hybrid domain generalization using source
domain and multiple latent domains as a new research scenario, and we attempt to train a …

… When our method is combined with MixStyle, it further promotes domain generalization
com… features that have undergone MixStyle, with domain information mixed between batches, …

… robust frequency-wise features by simulating new domains during the training process. …
Freq-MixStyle mixes feature statistics of two instances to enhance domain generalization, we …

… of [22] propose MixStyle, an algorithm that mixes the styles of training instances in each
mini-batch to increase the sample diversity of source domains. Another method that produces …

… MatchDG [37] suggested a domain generalization invariance condition via causal flow
between labels. However, many causality and domain generalization solutions rely heavily on …

… MixStyle [86] mixes up styles … MixStyle and DSU by a large margin. We also observe that
there is a slight performance drop on the source domain (CS) when applying DSU and MixStyle. …

… Using MixStyle, we generate diverse domain styles that make … the generalization ability of
models pre-trained by BN. … MixStyle into MetaBIN to overcome the limited source domain, as …

… the source domain(s), as well as discovering intermediate domains. … we show that the former
leads to better generalization results. … either MixStyle or our mixing with a probability p = 0.5. …