<ul><li>Data Augmentation (DA) plays a crucial role in enhancing the robustness and generalization of modern machine learning.</li><li>A new framework for optimizing Data Augmentation has been proposed in a recent research paper.</li><li>The framework treats augmentation parameters as model (hyper)parameters and optimizes the marginal likelihood using Bayesian model selection.</li><li>Experiments on computer vision tasks have shown that this approach improves calibration and enhances performance compared to fixed or no augmentation.</li></ul>

Optimizing Data Augmentation through Bayesian Model Selection

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