Metadata Enriched Multi-Instance Contrastive Learning for High-Quality Facial Skin Visual Representations
Utilizing self-supervised learning to learn meaningful representations from unlabeled data can be a cost-effective strategy, particularly in medical domains where expert labeling incurs high costs. Contrastive learning typically employs a single contrastive relationship based on individual instances...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2025.2462389 |
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