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Stochastic weight averaging (SWA) is an optimization technique used in training deep neural networks. It involves averaging multiple sets of weights collected at different points during the training process to improve generalization and model performance.
Stochastic weight averaging–Gaussian (SWAG) is a technique in machine learning that improves model generalization and uncertainty estimation by approximating the posterior distribution of neural network weights using Gaussian distributions derived from stochastic weight averaging. It enhances predictive performance and reliability in deep learning models.