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Domain adaptation via prompt learning
Unsupervised domain adaptation (UDA) aims to adapt models learned from a well-
annotated source domain to a target domain, where only unlabeled samples are given …
annotated source domain to a target domain, where only unlabeled samples are given …
Onenet: Enhancing time series forecasting models under concept drift by online ensembling
Online updating of time series forecasting models aims to address the concept drifting
problem by efficiently updating forecasting models based on streaming data. Many …
problem by efficiently updating forecasting models based on streaming data. Many …
Adanpc: Exploring non-parametric classifier for test-time adaptation
Many recent machine learning tasks focus to develop models that can generalize to unseen
distributions. Domain generalization (DG) has become one of the key topics in various fields …
distributions. Domain generalization (DG) has become one of the key topics in various fields …
Flatness-aware minimization for domain generalization
Abstract Domain generalization (DG) seeks to learn robust models that generalize well
under unknown distribution shifts. As a critical aspect of DG, optimizer selection has not …
under unknown distribution shifts. As a critical aspect of DG, optimizer selection has not …
Fault vibration model driven fault-aware domain generalization framework for bearing fault diagnosis
B Pang, Q Liu, Z Xu, Z Sun, Z Hao, Z Song - Advanced Engineering …, 2024 - Elsevier
Deep learning methods can learn effective representations from the data, simplifying the
fault diagnosis process and improving accuracy. However, the lack of data presents a …
fault diagnosis process and improving accuracy. However, the lack of data presents a …
A novel inter-domain attention-based adversarial network for aero-engine partial unsupervised cross-domain fault diagnosis
Recently, domain adaptation methods have been widely applied in the field of aero-engine
cross-domain fault diagnosis, which can effectively solve the problem of training and testing …
cross-domain fault diagnosis, which can effectively solve the problem of training and testing …
[HTML][HTML] WeedVision: A single-stage deep learning architecture to perform weed detection and segmentation using drone-acquired images
Deep learning (DL) inspired models have achieved tremendous success in locating target
weed species through bounding-box approach (single-stage models) or pixel-wise semantic …
weed species through bounding-box approach (single-stage models) or pixel-wise semantic …
Multimodal adaptive emotion transformer with flexible modality inputs on a novel dataset with continuous labels
Emotion recognition from physiological signals is a topic of widespread interest, and
researchers continue to develop novel techniques for perceiving emotions. However, the …
researchers continue to develop novel techniques for perceiving emotions. However, the …
MMDG-DTI: Drug–target interaction prediction via multimodal feature fusion and domain generalization
Recently, deep learning has become the essential methodology for Drug–Target Interaction
(DTI) prediction. However, the existing learning-based representation methods embed the …
(DTI) prediction. However, the existing learning-based representation methods embed the …
Tensorial multiview low-rank high-order graph learning for context-enhanced domain adaptation
Abstract Unsupervised Domain Adaptation (UDA) is a machine learning technique that
facilitates knowledge transfer from a labeled source domain to an unlabeled target domain …
facilitates knowledge transfer from a labeled source domain to an unlabeled target domain …