Gradient consistency regularization and entropy-driven dynamic distillation improve accuracy by up to 5% in long-tailed incremental learning, with strong gains in majority-to-minority task ordering.
Overcoming catastrophic forgetting in neural networks,
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Dynamic Distillation and Gradient Consistency for Robust Long-Tailed Incremental Learning
Gradient consistency regularization and entropy-driven dynamic distillation improve accuracy by up to 5% in long-tailed incremental learning, with strong gains in majority-to-minority task ordering.