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 …
[HTML][HTML] Domain shifts in dermoscopic skin cancer datasets: Evaluation of essential limitations for clinical translation
The limited ability of Convolutional Neural Networks to generalize to images from previously
unseen domains is a major limitation, in particular, for safety-critical clinical tasks such as …
unseen domains is a major limitation, in particular, for safety-critical clinical tasks such as …
Maad-face: A massively annotated attribute dataset for face images
Soft-biometrics play an important role in face biometrics and related fields since these might
lead to biased performances, threaten the user's privacy, or are valuable for commercial …
lead to biased performances, threaten the user's privacy, or are valuable for commercial …
DSFA: cross-scene domain style and feature adaptation for landslide detection from high spatial resolution images
Rapid and accurate landslide inventory mapping is significant for emergency rescue and
post-disaster reconstruction. Nowadays, deep learning methods exhibit excellent …
post-disaster reconstruction. Nowadays, deep learning methods exhibit excellent …
[PDF][PDF] Domain adaptation in reinforcement learning: a comprehensive and systematic study
A Farhadi, M Mirzarezaee, A Sharifi… - Journal of Zhejiang …, 2024 - researchgate.net
Reinforcement learning (RL) has shown significant potential for dealing with complex
decision-making problems. However, its performance relies heavily on the availability of a …
decision-making problems. However, its performance relies heavily on the availability of a …
A novel multiview predictive local adversarial network for partial transfer learning in cross-domain fault diagnostics
S Tan, K Wang, H Shi, B Song - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
In recent years, transfer learning technology has been widely developed in the field of fault
diagnosis and detection. At present, most transfer learning methods are based on the …
diagnosis and detection. At present, most transfer learning methods are based on the …
Domain Adaptation-Based deep learning model for forecasting and diagnosis of glaucoma disease
The main factor causing irreversible blindness is glaucoma. Early detection greatly reduces
the risk of further vision loss. To address this problem, we developed a domain adaptation …
the risk of further vision loss. To address this problem, we developed a domain adaptation …
Optimizing makespan and resource utilization for multi-DNN training in GPU cluster
Deep neural network (DNN) has been widely applied in many fields of artificial intelligence
(AI), gaining great popularity both in industry and academia. Increasing the size of DNN …
(AI), gaining great popularity both in industry and academia. Increasing the size of DNN …
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 …
Key Design Choices in Source-Free Unsupervised Domain Adaptation: An In-depth Empirical Analysis
This study provides a comprehensive benchmark framework for Source-Free Unsupervised
Domain Adaptation (SF-UDA) in image classification, aiming to achieve a rigorous empirical …
Domain Adaptation (SF-UDA) in image classification, aiming to achieve a rigorous empirical …