Open Set Domain Adaptation by Backpropagation

TL;DR

This paper introduces an adversarial training method for open set domain adaptation, enabling the model to distinguish between known and unknown target samples and improve transfer learning in scenarios with unseen classes.

Contribution

It proposes a novel adversarial approach that separates unknown from known target samples, addressing limitations of existing methods in open set domain adaptation.

Findings

Outperforms existing methods in open set domain adaptation tasks

Effectively distinguishes unknown target samples from known ones

Achieves significant margin improvements in various settings

Abstract

Numerous algorithms have been proposed for transferring knowledge from a label-rich domain (source) to a label-scarce domain (target). Almost all of them are proposed for a closed-set scenario, where the source and the target domain completely share the class of their samples. We call the shared class the \doublequote{known class.} However, in practice, when samples in target domain are not labeled, we cannot know whether the domains share the class. A target domain can contain samples of classes that are not shared by the source domain. We call such classes the \doublequote{unknown class} and algorithms that work well in the open set situation are very practical. However, most existing distribution matching methods for domain adaptation do not work well in this setting because unknown target samples should not be aligned with the source. In this paper, we propose a method for an open set domain adaptation scenario which utilizes adversarial training. A classifier is trained to make a boundary between the source and the target samples whereas a generator is trained to make target samples far from the boundary. Thus, we assign two options to the feature generator: aligning them with source known samples or rejecting them as unknown target samples. This approach allows extracting features that separate unknown target samples from known target samples. Our method was extensively evaluated in domain adaptation setting and outperformed other methods with a large margin in most settings.