[PDF][PDF] Deep unsupervised domain adaptation: A review of recent advances and perspectives
Deep learning has become the method of choice to tackle real-world problems in different
domains, partly because of its ability to learn from data and achieve impressive performance …
domains, partly because of its ability to learn from data and achieve impressive performance …
Parallel vision for intelligent transportation systems in metaverse: Challenges, solutions, and potential applications
Metaverse and intelligent transportation system (ITS) are disruptive technologies that have
the potential to transform the current transportation system by decreasing traffic accidents …
the potential to transform the current transportation system by decreasing traffic accidents …
Synthetic datasets for autonomous driving: A survey
Z Song, Z He, X Li, Q Ma, R Ming, Z Mao… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Autonomous driving techniques have been flourishing in recent years while thirsting for
huge amounts of high-quality data. However, it is difficult for real-world datasets to keep up …
huge amounts of high-quality data. However, it is difficult for real-world datasets to keep up …
Black-box unsupervised domain adaptation with bi-directional atkinson-shiffrin memory
Black-box unsupervised domain adaptation (UDA) learns with source predictions of target
data without accessing either source data or source models during training, and it has clear …
data without accessing either source data or source models during training, and it has clear …
Confmix: Unsupervised domain adaptation for object detection via confidence-based mixing
Abstract Unsupervised Domain Adaptation (UDA) for object detection aims to adapt a model
trained on a source domain to detect instances from a new target domain for which …
trained on a source domain to detect instances from a new target domain for which …
Unsupervised domain adaptation through dynamically aligning both the feature and label spaces
Q Tian, Y Zhu, H Sun, S Chen… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
In unsupervised domain adaptation (UDA), a target-domain model is trained by the
supervised knowledge from a source domain. Although UDA has recently received much …
supervised knowledge from a source domain. Although UDA has recently received much …
A parallel teacher for synthetic-to-real domain adaptation of traffic object detection
Large-scale synthetic traffic image datasets have been widely used to make compensate for
the insufficient data in real world. However, the mismatch in domain distribution between …
the insufficient data in real world. However, the mismatch in domain distribution between …
Cl3d: Unsupervised domain adaptation for cross-lidar 3d detection
Abstract Domain adaptation for Cross-LiDAR 3D detection is challenging due to the large
gap on the raw data representation with disparate point densities and point arrangements …
gap on the raw data representation with disparate point densities and point arrangements …
Unsupervised structure subdomain adaptation based the Contrastive Cluster Center for bearing fault diagnosis
P Chen, R Zhao, T He, K Wei, J Yuan - Engineering Applications of Artificial …, 2023 - Elsevier
Abstract Recently, Unsupervised Domain Adaptation (UDA) as one of the transfer learning
can handle the different data distributions and has been utilized in mechanical fault …
can handle the different data distributions and has been utilized in mechanical fault …
M3-UDA: A New Benchmark for Unsupervised Domain Adaptive Fetal Cardiac Structure Detection
The anatomical structure detection of fetal cardiac views is crucial for diagnosing fetal
congenital heart disease. In practice there is a large domain gap between different hospitals' …
congenital heart disease. In practice there is a large domain gap between different hospitals' …