7 papers
OSCS-SupCon: Orthogonal Sigmoid-based Common and Style Supervised Contrastive Learning for Robust Feature Disentanglement
Bin Wang, Fadi Dornaika
Supervised Contrastive Learning (SupCon) has achieved strong performance by explicitly modeling pairwise relationships among samples. However, existing SupCon-based methods suffer…
Decoding Matters: Efficient Mamba-Based Decoder with Distribution-Aware Deep Supervision for Medical Image Segmentation
Fares Bougourzi, Fadi Dornaika, Abdenour Hadid
Deep learning has achieved remarkable success in medical image segmentation, often reaching expert-level accuracy in delineating tumors and tissues. However, most existing approach…
Enhancing Semi-Supervised Multi-View Graph Convolutional Networks via Supervised Contrastive Learning and Self-Training
Huaiyuan Xiao, Fadi Dornaika, Jingjun Bi
The advent of graph convolutional network (GCN)-based multi-view learning provides a powerful framework for integrating structural information from heterogeneous views, enabling ef…
MCFCN: Multi-View Clustering via a Fusion-Consensus Graph Convolutional Network
Chenping Pei, Fadi Dornaika, Jingjun Bi
Existing Multi-view Clustering (MVC) methods based on subspace learning focus on consensus representation learning while neglecting the inherent topological structure of data. Desp…
Agentic AI: A Comprehensive Survey of Architectures, Applications, and Future Directions
Mohamad Abou Ali, Fadi Dornaika
Agentic AI represents a transformative shift in artificial intelligence, but its rapid advancement has led to a fragmented understanding, often conflating modern neural systems wit…
A Re-node Self-training Approach for Deep Graph-based Semi-supervised Classification on Multi-view Image Data
Jingjun Bi, Fadi Dornaika
Recently, graph-based semi-supervised learning and pseudo-labeling have gained attention due to their effectiveness in reducing the need for extensive data annotations. Pseudo-labe…