Self-Supervised NAS for Multimodal DNNs
Research Paper#Neural Architecture Search, Self-Supervised Learning, Multimodal Learning🔬 Research|Analyzed: Jan 3, 2026 06:25•
Published: Dec 31, 2025 11:30
•1 min read
•ArXivAnalysis
This paper addresses the challenge of designing multimodal deep neural networks (DNNs) using Neural Architecture Search (NAS) when labeled data is scarce. It proposes a self-supervised learning (SSL) approach to overcome this limitation, enabling architecture search and model pretraining from unlabeled data. This is significant because it reduces the reliance on expensive labeled data, making NAS more accessible for complex multimodal tasks.
Key Takeaways
- •Proposes a self-supervised learning (SSL) method for Neural Architecture Search (NAS) in multimodal DNNs.
- •Addresses the problem of limited labeled data in multimodal DNN architecture design.
- •Applies SSL to both architecture search and model pretraining.
- •Demonstrates the ability to design architectures from unlabeled data.
Reference / Citation
View Original"The proposed method applies SSL comprehensively for both the architecture search and model pretraining processes."