Unsupervised Open Domain Recognition by Semantic Discrepancy Minimization

TL;DR

This paper introduces a novel unsupervised open domain recognition method that minimizes semantic discrepancy to accurately classify both known and unknown categories in unlabeled target domains.

Contribution

It proposes the Semantic-Guided Matching Discrepancy (SGMD) and a joint learning framework UODTN for improved open domain recognition without labeled target data.

Findings

Outperforms existing methods in recognizing known and unknown categories.

Effectively reduces domain discrepancy through SGMD.

Achieves balanced classification across categories.

Abstract

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.