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Results show that forgetting depends not only on how much the model changes, but also on which parts of the model are allowed to change, which means that forgetting still increases as more blocks are trained and remains severe when the full backbone is updated.
Qualitative and mechanistic analyses indicate that explanation degradation is primarily associated with the collapse of class-discriminative gradients rather than the disappearance of feature activations, suggesting that model compression should be evaluated beyond aggregate performance.
A systematic evaluation of CL for MedVQA across diverse clinical objectives, including classification, multi-label classification, detection, cell counting, and report generation suggests that existing CL methods struggle to maintain stability-plasticity balance when tasks with different objectives and supervision form...
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