Kate Saenko

Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable\n Neural Distribution Alignment

2020 · arXiv (Cornell University)

Distribution alignment has many applications in deep learning, including\ndomain adaptation and unsupervised image-to-image translation. Most prior work\non unsupervised distribution alignment relies either on minimizing simple\nnon-parametric statistical distances such as maximum mean discrepancy or on\nadversarial alignment. …

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 …

Bias Mimicking: A Simple Sampling Approach for Bias Mitigation

2022 · arXiv (Cornell University)

Prior work has shown that Visual Recognition datasets frequently underrepresent bias groups $B$ (\eg Female) within class labels $Y$ (\eg Programmers). This dataset bias can lead to models that learn spurious correlations between class labels …