Enhancing open-set domain adaptation through unknown-filtering multi-classifier adversarial network.

Domain adaptation is a fundamental research problem that aims to address the domain shift issue during the transfer of a model from a labeled source domain to an unlabeled target domain. In traditional domain adaptation scenarios, it is assumed that the class spaces of both the source and target domains are identical. However, real-world applications often entail situations where the target domain contains private classes that are absent in the source domain. Forcing the alignment of these two domains may result in negative transfer. This specific concern is addressed by the emerging field of Open-set Domain Adaptation (OSDA). Previous OSDA methods attempted to align the known classes between the source and target domains while separating the unknown samples. However, these methods are inadequate in effectively discerning instances from the unknown classes. Therefore, we propose Enhancing Open-Set Domain Adaptation through Unknown-Filtering Multi-Classifier Adversarial Network (UFMCAN), which leverages multiple classifiers including a weighted auxiliary classifier, an open-set recognizer, a primary classifier and a three-way domain discriminator through adversarial learning to effectively filter instances from the unknown classes present in the target domain while concurrently aligning the source and target-known distribution. Experiments on extensive benchmarks (Office-31, Office-Home, VisDA-2017 and Office-Mix) demonstrate the superior performance of UFMCAN compared to existing state-of-the-art methods. • UFMCAN is proposed to address open set domain adaptation problem. • Multiple classifiers are used to filter unknown samples and align distribution. • Empirical results show the effectiveness of UFMCAN. [ABSTRACT FROM AUTHOR]