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Rethinking Task-Incremental Learning Baselines
A common goal of task-incremental methods is to design a network that can operate on minimal size, maintaining decent performance.
Adaptive Class Weight based Dual Focal Loss for Improved Semantic Segmentation
Our DFL method is an improvement on the recently reported Focal Loss (FL) cross-entropy function, which proposes a scaling method that puts more weight on the examples that are difficult to classify over those that are easy.
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