aiqbench analysis
This research introduces VISTA, an online self-distillation framework designed to mitigate "Trajectory Deviation," where models discard latent features despite maintaining high validation accuracy. By enforcing consistency along the optimization trajectory using expert anchors from earlier training states, the method improves robustness and generalization while significantly reducing storage overhead. The approach represents a meaningful advancement in optimizing deep learning convergence without relying on classical overfitting signals.
VISTA: Validation-Informed Trajectory Adaptation via Self-Distillation
arXiv:2604.12044v2 Announce Type: replace-cross Abstract: Deep learning models may converge to suboptimal solutions despite strong validation accuracy, masking an optimization failure we term Trajectory Deviation. This is because as training proceeds, models can abandon high generalization states for specific data sub-populations, thus discarding previously learned latent features without triggering classical overfitting signals. To address this problem we introduce VISTA, an online self-distillation framework that enforces consistency along the optimization trajectory. Using a validation-informed Marginal Coverage score, VISTA identifies expert anchors, which are earlier model states that retain specialized competence over distinct data regions. A coverage-weighted ensemble of these anchors is integrated online during training, regularizing the loss landscape and preserving mastered knowledge. When evaluated across multiple benchmarks, VISTA demonstrates improved robustness and generalization over standard training and prior self-distillation methods, while a lightweight implementation reduces storage overhead by 90% without performance loss.
Abstract:Deep learning models may converge to suboptimal solutions despite strong validation accuracy, masking an optimization failure we term Trajectory Deviation. This is because as training proceeds, models can abandon high generalization states for specific data sub-populations, thus discarding previously learned latent features without triggering classical overfitting signals. To address this problem we introduce VISTA, an online self-distillation framework that enforces consistency along the optimization trajectory. Using a validation-informed Marginal Coverage score, VISTA identifies expert anchors, which are earlier model states that retain specialized competence over distinct data regions. A coverage-weighted ensemble of these anchors is integrated online during training, regularizing the loss landscape and preserving mastered knowledge. When evaluated across multiple benchmarks, VISTA demonstrates improved robustness and generalization over standard training and prior self-distillation methods, while a lightweight implementation reduces storage overhead by 90% without performance loss.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2604.12044 [cs.LG]
(or arXiv:2604.12044v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2604.12044 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 232 words
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