NoFA-BC proposes a non-forgetting allocator using recursive least-squares and bi-level competition for improved knowledge allocation in class-incremental learning.
AIR: Ana- lytic imbalance rectifier for continual learning
2 Pith papers cite this work. Polarity classification is still indexing.
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WeatherRemover is a lightweight all-in-one adverse weather removal model that uses channel-wise attention, linear spatial reduction, and gating in a multi-scale transformer-UNet to restore images efficiently across rain, snow, and fog.
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Non-Forgetting Knowledge Allocation with Bi-level Competition for Class-Incremental Learning
NoFA-BC proposes a non-forgetting allocator using recursive least-squares and bi-level competition for improved knowledge allocation in class-incremental learning.
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WeatherRemover: All-in-one Adverse Weather Removal with Multi-scale Feature Map Compression
WeatherRemover is a lightweight all-in-one adverse weather removal model that uses channel-wise attention, linear spatial reduction, and gating in a multi-scale transformer-UNet to restore images efficiently across rain, snow, and fog.