Controlled vision experiments show that linear weight merging preserves shared knowledge but rapidly erases task-specific (unshared) knowledge, and merging sequentially trained models is safer than merging parallel-trained ones.
Loss surfaces, mode connectivity, and fast ensembling of DNNs
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Forgetting of task-specific knowledge in model merging-based continual learning
Controlled vision experiments show that linear weight merging preserves shared knowledge but rapidly erases task-specific (unshared) knowledge, and merging sequentially trained models is safer than merging parallel-trained ones.