Results 41 to 50 of about 2,905 (180)
IEEE Access, 2023 LiDAR semantic segmentation is receiving increased attention due to its deployment in autonomous driving applications. As LiDARs come often with other sensors such as RGB cameras, multi-modal approaches for this task have been developed, which however ...
Adriano Cardace +5 more
doaj +1 more source
IEEE Access, 2021 Unsupervised Domain Adaptation (UDA) for re-identification (re-ID) is a challenging task: to avoid a costly annotation of additional data, it aims at transferring knowledge from a domain with annotated data to a domain of interest with only unlabeled ...
Fabian Dubourvieux +4 more
doaj +1 more source
Structure preserved ordinal unsupervised domain adaptation
Electronic Research ArchiveUnsupervised domain adaptation (UDA) aims to transfer the knowledge from labeled source domain to unlabeled target domain. The main challenge of UDA stems from the domain shift between the source and target domains.
Qing Tian, Canyu Sun
doaj +1 more source
Unsupervised Domain Adaptation using Graph Transduction Games
, 2019 Unsupervised domain adaptation (UDA) amounts to assigning class labels to the unlabeled instances of a dataset from a target domain, using labeled instances of a dataset from a related source domain.
Aslan, Sinem +5 more
core +1 more source
Zero-Shot Deep Domain Adaptation
, 2018 Domain adaptation is an important tool to transfer knowledge about a task (e.g. classification) learned in a source domain to a second, or target domain. Current approaches assume that task-relevant target-domain data is available during training.
B Sun +7 more
core +1 more source
, 2022 Recent works on unsupervised domain adaptation (UDA) focus on the selection of good pseudo-labels as surrogates for the missing labels in the target data. However, source domain bias that deteriorates the pseudo-labels can still exist since the shared network of the source and target domains are typically used for the pseudo-label selections.
Zhang, Can, Lee, Gim Hee
openaire +2 more sources
Addressing materials’ microstructure diversity using transfer learning
npj Computational Materials, 2022 Materials’ microstructures are signatures of their alloying composition and processing history. Automated, quantitative analyses of microstructural constituents were lately accomplished through deep learning approaches.
Aurèle Goetz +6 more
doaj +1 more source
Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation
, 2018 While representation learning aims to derive interpretable features for describing visual data, representation disentanglement further results in such features so that particular image attributes can be identified and manipulated.
Chiu, Wei-Chen +5 more
core +1 more source
Test-time unsupervised domain adaptation [PDF]
, 2020 Convolutional neural networks trained on publicly available medical imaging datasets (source domain) rarely generalise to different scanners or acquisition protocols (target domain). This motivates the active field of domain adaptation.
Cardoso, MJ +5 more
core
Unified Deep Supervised Domain Adaptation and Generalization
, 2017 This work provides a unified framework for addressing the problem of visual supervised domain adaptation and generalization with deep models. The main idea is to exploit the Siamese architecture to learn an embedding subspace that is discriminative, and ...
Adjeroh, Donald A. +3 more
core +1 more source