Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain Adaptation

by   Li Zhong, et al.

In the unsupervised open set domain adaptation (UOSDA), the target domain contains unknown classes that are not observed in the source domain. Researchers in this area aim to train a classifier to accurately: 1) recognize unknown target data (data with unknown classes) and, 2) classify other target data. To achieve this aim, a previous study has proven an upper bound of the target-domain risk, and the open set difference, as an important term in the upper bound, is used to measure the risk on unknown target data. By minimizing the upper bound, a shallow classifier can be trained to achieve the aim. However, if the classifier is very flexible (e.g., deep neural networks (DNNs)), the open set difference will converge to a negative value when minimizing the upper bound, which causes an issue where most target data are recognized as unknown data. To address this issue, we propose a new upper bound of target-domain risk for UOSDA, which includes four terms: source-domain risk, ϵ-open set difference (Δ_ϵ), a distributional discrepancy between domains, and a constant. Compared to the open set difference, Δ_ϵ is more robust against the issue when it is being minimized, and thus we are able to use very flexible classifiers (i.e., DNNs). Then, we propose a new principle-guided deep UOSDA method that trains DNNs via minimizing the new upper bound. Specifically, source-domain risk and Δ_ϵ are minimized by gradient descent, and the distributional discrepancy is minimized via a novel open-set conditional adversarial training strategy. Finally, compared to existing shallow and deep UOSDA methods, our method shows the state-of-the-art performance on several benchmark datasets, including digit recognition (MNIST, SVHN, USPS), object recognition (Office-31, Office-Home), and face recognition (PIE).


page 1

page 12

page 15


Open Set Domain Adaptation: Theoretical Bound and Algorithm

Unsupervised domain adaptation for classification tasks has achieved gre...

How does the Combined Risk Affect the Performance of Unsupervised Domain Adaptation Approaches?

Unsupervised domain adaptation (UDA) aims to train a target classifier w...

Against Adversarial Learning: Naturally Distinguish Known and Unknown in Open Set Domain Adaptation

Open set domain adaptation refers to the scenario that the target domain...

Some Theory For Practical Classifier Validation

We compare and contrast two approaches to validating a trained classifie...

On the Effectiveness of Image Rotation for Open Set Domain Adaptation

Open Set Domain Adaptation (OSDA) bridges the domain gap between a label...

Algorithms and Theory for Supervised Gradual Domain Adaptation

The phenomenon of data distribution evolving over time has been observed...

Unsupervised Open Domain Recognition by Semantic Discrepancy Minimization

We address the unsupervised open domain recognition (UODR) problem, wher...

Please sign up or login with your details

Forgot password? Click here to reset