SEM-HD consistently improves long-horizon AUC and pAUC over longitudinal models evaluated without history, particularly in the clinically relevant low false-positive-rate region and recovers much of the performance gap with respect to full-history inference across datasets and backbones.
The Relearning Score (RS), which jointly measures forget-class recovery and retain-accuracy preservation, and report class-matched $\Delta$RS relative to a retrained reference, is introduced.
Zahra Dehghani, Pablo Piantanida, Mohammadhadi Shateri· 0 citations
This work proposes MeanFlow-Transfer, which maps heterogeneous source outputs into a shared velocity representation, uses it to initialize an MF generator from the source weights, and optimizes an MF objective on the target domain, and introduces Continuous Adversarial MeanFlow, a post-training stage that extends conti...
Yara Bahram, Zahra Dehghani, M. Desbos et al.· 0 citations
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