Stochastic Weight Averaging

Stochastic Weight Averaging (SWA) is a technique used to improve the generalization performance of neural networks by averaging model weights sampled during training, often employing cyclical or constant learning rate schedules. Current research focuses on adapting SWA to various architectures and tasks, including large language models and long-tailed classification, and exploring its combination with other techniques like low-rank adaptation and early stopping to enhance efficiency and robustness. This approach offers a computationally inexpensive way to improve model generalization and calibration, impacting diverse fields from natural language processing and computer vision to time-series analysis and potentially leading to more reliable and robust machine learning models across applications.