PODS is a plug-and-play oscillatory data-volume scheduler that alternates low-ratio regularization phases with high-ratio recovery phases to improve data selection efficiency across training tasks.
A clip-powered framework for robust and generalizable data selection.arXiv preprint arXiv:2410.11215
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UNVERDICTED 3representative citing papers
X-Shift is a grey-box attack that perturbs patch-level visual features in VLMs to shift explanation heatmaps without changing the predicted output.
Data Agent learns a co-evolving sample selection policy end-to-end that accelerates training by over 50% on ImageNet-1k and MMLU with no performance loss.
citing papers explorer
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Beyond What to Select: A Plug-and-play Oscillatory Data-Volume Scheduling for Efficient Model Training
PODS is a plug-and-play oscillatory data-volume scheduler that alternates low-ratio regularization phases with high-ratio recovery phases to improve data selection efficiency across training tasks.
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Right Predictions, Misleading Explanations: On the Vulnerability of Vision-Language Model Explanations
X-Shift is a grey-box attack that perturbs patch-level visual features in VLMs to shift explanation heatmaps without changing the predicted output.
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Data Agent: Learning to Select Data via End-to-End Dynamic Optimization
Data Agent learns a co-evolving sample selection policy end-to-end that accelerates training by over 50% on ImageNet-1k and MMLU with no performance loss.