Deep learning has become the method of choice to tackle real-world problems in different
domains, partly because of its ability to learn from data and achieve impressive performance …

Optimal transport (OT) theory can be informally described using the words of the French
mathematician Gaspard Monge (1746–1818): A worker with a shovel in hand has to move a …

We present rectified flow, a surprisingly simple approach to learning (neural) ordinary
differential equation (ODE) models to transport between two empirically observed …

Deep learning has produced state-of-the-art results for a variety of tasks. While such
approaches for supervised learning have performed well, they assume that training and …

In this work, we connect two distinct concepts for unsupervised domain adaptation: feature
distribution alignment between domains by utilizing the task-specific decision boundary and …

Due to the ability of deep neural nets to learn rich representations, recent advances in
unsupervised domain adaptation have focused on learning domain-invariant features that …

Abstract Domain generalization aims to learn a classification model from multiple source
domains and generalize it to unseen target domains. A critical problem in domain …

Domain adaptation has become a prominent problem setting in machine learning and
related fields. This review asks the question: How can a classifier learn from a source …

Adversarial learning has been embedded into deep networks to learn disentangled and
transferable representations for domain adaptation. Existing adversarial domain adaptation …

Recent work has shown that the accuracy of machine learning models can vary substantially
when evaluated on a distribution that even slightly differs from that of the training data. As a …