The paper proposes splitting real-world degradation into four tasks and adaptively rebalancing each task's training data volume, reporting consistent gains over prior super-resolution methods.
Multinet++: Multi-stream feature aggregation and geometric loss strategy for multi-task learning
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Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution
The paper proposes splitting real-world degradation into four tasks and adaptively rebalancing each task's training data volume, reporting consistent gains over prior super-resolution methods.