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 …
Soda: Detecting covid-19 in chest x-rays with semi-supervised open set domain adaptation
Due to the shortage of COVID-19 viral testing kits, radiology imaging is used to complement
the screening process. Deep learning based methods are promising in automatically …
the screening process. Deep learning based methods are promising in automatically …
Subdomain adaptation via correlation alignment with entropy minimization for unsupervised domain adaptation
Unsupervised domain adaptation (UDA) is a well-explored domain in transfer learning,
finding applications across various real-world scenarios. The central challenge in UDA lies …
finding applications across various real-world scenarios. The central challenge in UDA lies …
Dual adversarial domain adaptation
Unsupervised domain adaptation aims at transferring knowledge from the labeled source
domain to the unlabeled target domain. Previous adversarial domain adaptation methods …
domain to the unlabeled target domain. Previous adversarial domain adaptation methods …
Multiple adversarial domains adaptation approach for mitigating adversarial attacks effects
Although neural networks are near achieving performance similar to humans in many tasks,
they are susceptible to adversarial attacks in the form of a small, intentionally designed …
they are susceptible to adversarial attacks in the form of a small, intentionally designed …
Night-time vehicle model recognition based on domain adaptation
Owing to the low brightness, low contrast, and high labeling difficulty of night-time vehicle
images, night-time vehicle model recognition (NVMR) faces significant challenges. To …
images, night-time vehicle model recognition (NVMR) faces significant challenges. To …
Adversarial Source Generation for Source-Free Domain Adaptation
Unsupervised domain adaptation aims to transfer the knowledge learned from a labeled
source domain to an unlabeled target domain with different data distributions. However, in …
source domain to an unlabeled target domain with different data distributions. However, in …
Class-rebalanced wasserstein distance for multi-source domain adaptation
Q Wang, S Wang, B Wang - Applied Intelligence, 2023 - Springer
In the study of machine learning, multi-source domain adaptation (MSDA) handles multiple
datasets which are collected from different distributions by using domain-invariant …
datasets which are collected from different distributions by using domain-invariant …
A Class-aware Optimal Transport Approach with Higher-Order Moment Matching for Unsupervised Domain Adaptation
Unsupervised domain adaptation (UDA) aims to transfer knowledge from a labeled source
domain to an unlabeled target domain. In this paper, we introduce a novel approach called …
domain to an unlabeled target domain. In this paper, we introduce a novel approach called …
Letting Go of Self-Domain Awareness: Multi-Source Domain-Adversarial Generalization via Dynamic Domain-Weighted Contrastive Transfer Learning
Abstract Domain generalization (DG), which aims to learn a model that can generalize to an
unseen target domain, has recently attracted increasing research interest. A major approach …
unseen target domain, has recently attracted increasing research interest. A major approach …