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arXiv AI By Eli Corn, Daphna Weinshall

VISTA: Validation-Informed Trajectory Adaptation via Self-Distillation

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VISTA is an online self‑distillation framework that enforces consistency along a deep learning model’s optimization trajectory. It uses a validation‑informed Marginal Coverage score to identify earlier model states—called expert anchors—that retain specialized competence over distinct data regions. By integrating a coverage‑weighted ensemble of these anchors during training, VISTA regularizes the loss landscape, preserves learned knowledge, and improves robustness and generalization while cutting storage overhead by 90%.

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