Tag: transformer encoder

A new dynamic graph learning framework combines graph convolutions, transformers, and hypergraph temporal cues to accurately predict future node importance ...

Researchers developed DETS-Net, a dual-encoder deep learning model combining CNN and Transformer architectures to accurately extract mountainous terraced fields from ...

Researchers developed a Bayesian CNN-Transformer hybrid architecture that leverages uncertainty quantification to improve intermittent fault detection accuracy in wireless sensor ...

Researchers developed a unified spatio-temporal graph attention transformer framework that achieved 85.9 percent accuracy on DEAP and 91.3 percent accuracy ...

Researchers developed EARS-Net, a hybrid deep learning model combining generative oversampling, dual attention, and transformers that achieves superior facial expression ...

Researchers have developed MGTF-Net, a multi-backbone transformer fusion network that achieved 98.73 percent accuracy in classifying diseases across mango and ...

A hybrid neural network combining moment channel attention with a Transformer markedly reduced cross-subject performance variability in EEG emotion recognition ...

A deep learning framework named TransCRNN integrates convolutional networks, recurrent units, attention layers, and transformers with handcrafted acoustic features to ...

A four-stage hybrid framework combining convolutional spectral compression, a Transformer encoder, and U-Net spatial extraction reports 99.85% overall accuracy on ...

A hybrid ResNet50 and Transformer model achieved 89.7 percent accuracy on the FER2013 facial emotion benchmark, with an ablation study ...