15 citations · 40 across the 6 of their papers we have counts for
15 papers
Tune it the Right Way: Unsupervised Validation of Domain Adaptation via Soft Neighborhood Density
Kuniaki Saito, Donghyun Kim, Piotr Teterwak +3
Unsupervised domain adaptation (UDA) methods can dramatically improve generalization on unlabeled target domains. However, optimal hyper-parameter selection is critical to achievin…
VisDA-2021 Competition Universal Domain Adaptation to Improve Performance on Out-of-Distribution Data
Dina Bashkirova, Dan Hendrycks, Donghyun Kim +5
Progress in machine learning is typically measured by training and testing a model on the same distribution of data, i.e., the same domain. This over-estimates future accuracy on o…
OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers
Kuniaki Saito, Donghyun Kim, Kate Saenko
Semi-supervised learning (SSL) is an effective means to leverage unlabeled data to improve a model's performance. Typical SSL methods like FixMatch assume that labeled and unlabele…
OVANet: One-vs-All Network for Universal Domain Adaptation
Kuniaki Saito, Kate Saenko
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 unkno…
COCO-FUNIT: Few-Shot Unsupervised Image Translation with a Content Conditioned Style Encoder
Kuniaki Saito, Kate Saenko, Ming-Yu Liu
Unsupervised image-to-image translation intends to learn a mapping of an image in a given domain to an analogous image in a different domain, without explicit supervision of the ma…
Cross-domain Self-supervised Learning for Domain Adaptation with Few Source Labels
Donghyun Kim, Kuniaki Saito, Tae-Hyun Oh +3
Existing unsupervised domain adaptation methods aim to transfer knowledge from a label-rich source domain to an unlabeled target domain. However, obtaining labels for some source d…