Unsupervised Domain Adaptation (UDA) aims to classify unlabeled target domain by transferring knowledge from labeled source with shift. Most of the existing UDA methods try mitigate adverse impact induced shift via reducing discrepancy. However, such approaches easily suffer a notorious mode collapse issue due lack labels in domain. Naturally, one effective ways this is reliably estimate pseudo...

Unsupervised domain adaptation aims to transfer knowledge from a labeled source an unlabeled target domain. Previous methods focus on learning domain-invariant features decrease the discrepancy between feature distributions as well minimizing error and have made remarkable progress. However, recently proposed theory reveals that such strategy is not sufficient for successful adaptation. It show...

Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a fully-labeled source different unlabeled target domain. Most existing UDA methods learn domain-invariant feature representations by minimizing distances across domains. In this work, we build upon contrastive self-supervised learning align features so as reduce the discrepancy between training and testing sets. Explo...

This paper proposes an importance weighted adversarial nets-based method for unsupervised domain adaptation, specific for partial domain adaptation where the target domain has less number of classes compared to the source domain. Previous domain adaptation methods generally assume the identical label spaces, such that reducing the distribution divergence leads to feasible knowledge transfer. Ho...

Survival analysis is the branch of statistics that studies relation between characteristics living entities and their respective survival times, taking into account partial information held by censored cases. A good can, for example, determine whether one medical treatment a group patients better than another. With rise machine learning, can be modeled as learning function maps studied to times...