A Framework of CWAEE is passed through the network and gets its calibrated score p c i, c C

For a better understanding of our method, we give the framework of CWAEE. We use the outputs of the one-vs-rest classifiers to detect known and unknown classes in unlabeled data. Then, the class-wise adaptive threshold is calculated with a two-component beta mixture model (BMM) which models the score distributions of known classes and unknown classes in an unsupervised way. The entire process is summarized in Figure 5. Figure 5: The process of detecting known and unknown classes. For Domain Generalization, it is important to exploit the inter-domain information which includes domain-dependent styles and domain-invariant semantics.