Towards domain adaptation with open-set target data: Review of theory and computer vision applications R1# C1
Open-set domain adaptation is a develo** and practical solution to training deep networks
using unlabeled data which have been collected among unknown data and are under …
using unlabeled data which have been collected among unknown data and are under …
Cross-domain open-set machinery fault diagnosis based on adversarial network with multiple auxiliary classifiers
Cross-domain fault diagnosis methods based on transfer learning attempt to leverage
knowledge from a domain with sufficient labeled samples to a different but related domain …
knowledge from a domain with sufficient labeled samples to a different but related domain …
Interactive dual adversarial neural network framework: An open-set domain adaptation intelligent fault diagnosis method of rotating machinery
The domain-adaptation technique has been proven to be able to resolve the fault diagnosis
under various working conditions. Most research presumes that the health states in the …
under various working conditions. Most research presumes that the health states in the …
Multi-scale adversarial network for vehicle detection in UAV imagery
Abstract Vehicle detection in Unmanned Aerial Vehicle (UAV) imagery plays a crucial role in
a variety of applications. However, UAVs are usually small, very maneuverable, and can …
a variety of applications. However, UAVs are usually small, very maneuverable, and can …
A self-supervised-driven open-set unsupervised domain adaptation method for optical remote sensing image scene classification and retrieval
S Wang, D Hou, H **ng - IEEE Transactions on Geoscience …, 2023 - ieeexplore.ieee.org
Unsupervised domain adaptation (UDA) is an important solution to reduce the bias between
the labeled source domain and the unlabeled target domain. It has attracted more attention …
the labeled source domain and the unlabeled target domain. It has attracted more attention …
PSDC: A prototype-based shared-dummy classifier model for open-set domain adaptation
Open-set domain adaptation (OSDA) aims to achieve knowledge transfer in the presence of
both domain shift and label shift, which assumes that there exist additional unknown target …
both domain shift and label shift, which assumes that there exist additional unknown target …
Decompose to adapt: Cross-domain object detection via feature disentanglement
Recent advances in unsupervised domain adaptation (UDA) techniques have witnessed
great success in cross-domain computer vision tasks, enhancing the generalization ability of …
great success in cross-domain computer vision tasks, enhancing the generalization ability of …
Transferable SAR image classification crossing different satellites under open set condition
For synthetic aperture radar (SAR) image classification problem, we need to take into
account unlabeled datasets containing unknown classes crossing different satellites. In this …
account unlabeled datasets containing unknown classes crossing different satellites. In this …
Seeing through darkness: Visual localization at night via weakly supervised learning of domain invariant features
Long term visual localization has to conquer the problem of matching images with dramatic
photometric changes caused by different seasons, natural and man-made illumination …
photometric changes caused by different seasons, natural and man-made illumination …
Open-set domain adaptation for scene classification using multi-adversarial learning
Abstract Domain adaptation methods are able to transfer knowledge across different
domains, tackling multi-sensor, multi-temporal or cross-regional remote sensing scenarios …
domains, tackling multi-sensor, multi-temporal or cross-regional remote sensing scenarios …