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Learning How to Read Hot: A Practical Guide
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- Pseudo-labeling via DBSCAN/k-means; iterative refinement. 4.2 Domain adaptation via style transfer and contrastive learning
- GAN-based image translation (e.g., CycleGAN) to reduce style gap; contrastive losses for domain-invariant features. 4.3 Self-supervised and contrastive learning
- Pretext tasks, MoCo/SimCLR adaptations, camera-aware negatives/positives.
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