New Trends in Representation Learning
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: 15 April 2026 | Viewed by 394
Special Issue Editors
Interests: machine learning; computer vision; multimedia
Interests: machine learning; representation learning
Special Issue Information
Dear Colleagues,
Representation learning stands at the forefront of artificial intelligence research, driving paradigm shifts across domains ranging from natural language processing to biomedical informatics. The rapid evolution of self-supervised learning, multimodal fusion, and geometric deep learning has unlocked unprecedented capabilities in extracting hierarchical patterns from complex data. However, fundamental challenges persist in scalability, interpretability, and cross-domain generalization, necessitating novel methodologies to bridge these gaps.
This Special Issue of Electronics seeks to explore cutting-edge advances that redefine how machines capture and utilize semantic, structural, and causal representations. We focus on architectures that transcend traditional feature engineering, enabling autonomous discovery of transferable knowledge across modalities and tasks—a capability that is critical for real-world applications in healthcare, robotics, and scientific discovery.
We invite original research and comprehensive reviews that address topics including (but not limited to) the following themes:
- Fundamental representation learning: self-supervised and contrastive learning, disentangled representation learning, and causal representation learning;
- Graph representation learning;
- Generative and adversarial representation learning;
- Efficient and lightweight representations;
- Representation learning for transfer learning and domain adaptation;
- Representation learning for interpretable machine learning;
- Representation learning for cross-modal learning;
- Representation learning in other domains, e.g., recommender systems, cybersecurity, natural language processing, and Large Language Models.
We look forward to receiving your contributions.
Dr. Mengmeng Jing
Dr. Liangjian Wen
Dr. Ye Li
Guest Editors
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Keywords
- representation learning
- self-supervised learning
- transfer learning
- cross-modal learning
- interpretable representation learning
- graph representation learning
- causal representation learning
- adversarial learning
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