Kuniaki Saito

Asymmetric Tri-training for Unsupervised Domain Adaptation

2017 · arXiv (Cornell University)

Deep-layered models trained on a large number of labeled samples boost the accuracy of many tasks. It is important to apply such models to different domains because collecting many labeled samples in various domains is …

OVANet: One-vs-All Network for Universal Domain Adaptation

2021 · 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Universal Domain Adaptation (UNDA) aims to handle both domain-shift and category-shift between two datasets, where the main challenge is to transfer knowledge while rejecting "unknown" classes which are absent in the labeled source data but …