Tag: self-supervised learning

Researchers at ETH Zurich and Roche have combined active learning with geometric and self-supervised deep learning to predict C–H borylation ...

A foundation model-based AI system called WeedNet identifies 1,593 weed species with 91.02 percent accuracy and reaches 97.38 percent accuracy ...

Researchers at ETH Zürich have shown that self-supervised pretraining on simulated detector data can teach a single neural encoder to ...

Researchers have built a hybrid malware detection framework combining abstract syntax trees, L-moments, graph analysis, and quantum-inspired neural networks that ...

Researchers at Shanghai Maritime University have developed STTF, a self-supervised framework that fuses road network topology with multi-granularity temporal information ...

A new graph-structured Transformer model trained with self-supervised learning and community-consensus pseudo-labeling achieves record F1-scores for detecting cryptocurrency money laundering ...

A new self-supervised framework called AnomalyMHKD combines self-distillation, cross-architecture distillation and dynamic teacher freezing to detect industrial anomalies with up ...

Researchers have developed a style-aware attention transformer that recognizes fine-grained interior design styles by focusing on textures, materials, and color ...

Researchers have introduced RPGCN, a relation-preserving graph convolutional network that combats oversmoothing and relational dilution to outperform state-of-the-art baselines across ...

Researchers have developed a dual-channel graph completion network that reconstructs missing node features and structure simultaneously while performing well even ...