Reusing the task-specific classifier as a discriminator: Discriminator-free adversarial domain adaptation
Adversarial learning has achieved remarkable performances for unsupervised domain
adaptation (UDA). Existing adversarial UDA methods typically adopt an additional …
adaptation (UDA). Existing adversarial UDA methods typically adopt an additional …
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 …
Brain–computer interfacing using functional near-infrared spectroscopy (fNIRS)
K Paulmurugan, V Vijayaragavan, S Ghosh… - Biosensors, 2021 - mdpi.com
Functional Near-Infrared Spectroscopy (fNIRS) is a wearable optical spectroscopy system
originally developed for continuous and non-invasive monitoring of brain function by …
originally developed for continuous and non-invasive monitoring of brain function by …
Domain impression: A source data free domain adaptation method
VK Kurmi, VK Subramanian… - Proceedings of the …, 2021 - openaccess.thecvf.com
Unsupervised Domain adaptation methods solve the adaptation problem for an unlabeled
target set, assuming that the source dataset is available with all labels. However, the …
target set, assuming that the source dataset is available with all labels. However, the …
Multi-source unsupervised domain adaptation via pseudo target domain
CX Ren, YH Liu, XW Zhang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Multi-source domain adaptation (MDA) aims to transfer knowledge from multiple source
domains to an unlabeled target domain. MDA is a challenging task due to the severe …
domains to an unlabeled target domain. MDA is a challenging task due to the severe …
Discriminative adversarial domain adaptation
Given labeled instances on a source domain and unlabeled ones on a target domain,
unsupervised domain adaptation aims to learn a task classifier that can well classify target …
unsupervised domain adaptation aims to learn a task classifier that can well classify target …
U-cam: Visual explanation using uncertainty based class activation maps
Understanding and explaining deep learning models is an imperative task. Towards this, we
propose a method that obtains gradient-based certainty estimates that also provide visual …
propose a method that obtains gradient-based certainty estimates that also provide visual …
A survey on adversarial domain adaptation
M HassanPour Zonoozi, V Seydi - Neural Processing Letters, 2023 - Springer
Having a lot of labeled data is always a problem in machine learning issues. Even by
collecting lots of data hardly, shift in data distribution might emerge because of differences in …
collecting lots of data hardly, shift in data distribution might emerge because of differences in …
An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision
This review article comprehensively delves into the rapidly evolving field of domain
adaptation in computer and robotic vision. It offers a detailed technical analysis of the …
adaptation in computer and robotic vision. It offers a detailed technical analysis of the …
Discriminative radial domain adaptation
Domain adaptation methods reduce domain shift typically by learning domain-invariant
features. Most existing methods are built on distribution matching, eg, adversarial domain …
features. Most existing methods are built on distribution matching, eg, adversarial domain …