Large-scale labeled training datasets have enabled deep neural networks to excel across a
wide range of benchmark vision tasks. However, in many applications, it is prohibitively …

The aging process results in multiple traceable footprints, which can be quantified and used
to estimate an organism's age. Examples of such aging biomarkers include epigenetic …

Conventional domain adaptation (DA) techniques aim to improve domain transferability by
learning domain-invariant representations; while concurrently preserving the task …

State-of-the-art deep learning models are often trained with a large amount of costly labeled
training data. However, requiring exhaustive manual annotations may degrade the model's …

We are concerned with a worst-case scenario in model generalization, in the sense that a
model aims to perform well on many unseen domains while there is only one single domain …

Abstract Domain adaptation for semantic image segmentation is very necessary since
manually labeling large datasets with pixel-level labels is expensive and time consuming …

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 …

Abstract Domain adaptation aims to transfer knowledge in the presence of the domain gap.
Existing domain adaptation methods rely on rich prior knowledge about the relationship …

Abstract Domain adaptation enables the learner to safely generalize into novel
environments by mitigating domain shifts across distributions. Previous works may not …

Unsupervised domain adaptive person re-identification (UDA re-ID) aims at transferring the
labeled source domain's knowledge to improve the model's discriminability on the unlabeled …