Transfer adaptation learning: A decade survey
The world we see is ever-changing and it always changes with people, things, and the
environment. Domain is referred to as the state of the world at a certain moment. A research …
environment. Domain is referred to as the state of the world at a certain moment. A research …
[HTML][HTML] Deep learning and transfer learning for device-free human activity recognition: A survey
Device-free activity recognition plays a crucial role in smart building, security, and human–
computer interaction, which shows its strength in its convenience and cost-efficiency …
computer interaction, which shows its strength in its convenience and cost-efficiency …
Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation
Unsupervised domain adaptation (UDA) aims to leverage the knowledge learned from a
labeled source dataset to solve similar tasks in a new unlabeled domain. Prior UDA …
labeled source dataset to solve similar tasks in a new unlabeled domain. Prior UDA …
Deep subdomain adaptation network for image classification
For a target task where the labeled data are unavailable, domain adaptation can transfer a
learner from a different source domain. Previous deep domain adaptation methods mainly …
learner from a different source domain. Previous deep domain adaptation methods mainly …
Model adaptation: Unsupervised domain adaptation without source data
In this paper, we investigate a challenging unsupervised domain adaptation setting---
unsupervised model adaptation. We aim to explore how to rely only on unlabeled target data …
unsupervised model adaptation. We aim to explore how to rely only on unlabeled target data …
Source data-absent unsupervised domain adaptation through hypothesis transfer and labeling transfer
Unsupervised domain adaptation (UDA) aims to transfer knowledge from a related but
different well-labeled source domain to a new unlabeled target domain. Most existing UDA …
different well-labeled source domain to a new unlabeled target domain. Most existing UDA …
Learning robust global representations by penalizing local predictive power
Despite their renowned in-domain predictive power, convolutional neural networks are
known to rely more on high-frequency patterns that humans deem superficial than on low …
known to rely more on high-frequency patterns that humans deem superficial than on low …
Fixbi: Bridging domain spaces for unsupervised domain adaptation
Unsupervised domain adaptation (UDA) methods for learning domain invariant
representations have achieved remarkable progress. However, most of the studies were …
representations have achieved remarkable progress. However, most of the studies were …
Adversarial domain adaptation with domain mixup
Recent works on domain adaptation reveal the effectiveness of adversarial learning on
filling the discrepancy between source and target domains. However, two common …
filling the discrepancy between source and target domains. However, two common …
Cross-domain gradient discrepancy minimization for unsupervised domain adaptation
Abstract Unsupervised Domain Adaptation (UDA) aims to generalize the knowledge learned
from a well-labeled source domain to an unlabled target domain. Recently, adversarial …
from a well-labeled source domain to an unlabled target domain. Recently, adversarial …