  We address the unsupervised open domain recognition (UODR) problem, where
categories in labeled source domain S is only a subset of those in unlabeled
target domain T. The task is to correctly classify all samples in T including
known and unknown categories. UODR is challenging due to the domain
discrepancy, which becomes even harder to bridge when a large number of unknown
categories exist in T. Moreover, the classification rules propagated by graph
CNN (GCN) may be distracted by unknown categories and lack generalization
capability. To measure the domain discrepancy for asymmetric label space
between S and T, we propose Semantic-Guided Matching Discrepancy (SGMD), which
first employs instance matching between S and T, and then the discrepancy is
measured by a weighted feature distance between matched instances. We further
design a limited balance constraint to achieve a more balanced classification
output on known and unknown categories. We develop Unsupervised Open Domain
Transfer Network (UODTN), which learns both the backbone classification network
and GCN jointly by reducing the SGMD, enforcing the limited balance constraint
and minimizing the classification loss on S. UODTN better preserves the
semantic structure and enforces the consistency between the learned domain
invariant visual features and the semantic embeddings. Experimental results
show superiority of our method on recognizing images of both known and unknown
categories.
