Different valid temporal partitions of the same streaming dataset can produce materially different rankings and performance numbers for continual learning methods.
IEEE transactions on pattern analysis and machine intelligence , volume=
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HEDP uses energy regularization inspired by Helmholtz free energy plus hybrid energy-distance weighting in prompts to improve domain selection and achieve a 2.57% accuracy gain on benchmarks like CORe50 while mitigating catastrophic forgetting.
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Temporal Taskification in Streaming Continual Learning: A Source of Evaluation Instability
Different valid temporal partitions of the same streaming dataset can produce materially different rankings and performance numbers for continual learning methods.
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HEDP: A Hybrid Energy-Distance Prompt-based Framework for Domain Incremental Learning
HEDP uses energy regularization inspired by Helmholtz free energy plus hybrid energy-distance weighting in prompts to improve domain selection and achieve a 2.57% accuracy gain on benchmarks like CORe50 while mitigating catastrophic forgetting.