Joint Learning Framework for Dehazing and Deraining

Perspective: Joint learning can share priors, but negative transfer must be controlled carefully.

Research Question

This article focuses on adverse-weather restoration: improving interpretability, stability, and deployability while preserving strong performance.

Method Perspective

1. Define task constraints before increasing model complexity.
2. Use both perceptual and objective metrics for evaluation.
3. Replay failure cases during training to reduce tail-risk.

Evaluation Suggestions

• Report not only peak scores, but also variance and worst-case behavior.
• Add cross-domain validation to avoid single-dataset overfitting.
• Include latency and memory costs for engineering decisions.

Representative Papers and Links

• RT-DETR (2023)
• AutoAugment (2019)
• MIRNet (2020)

Production Insight

Joint learning can share priors, but negative transfer must be controlled carefully. In practical delivery, I strongly recommend using a minimum loop of failure replay, metric dashboarding, and rollback plans.