VI-PANN: Harnessing Transfer Learning and Uncertainty-Aware Variational Inference for Improved Generalization in Audio Pattern Recognition

In this study, the authors propose VI-PANNs as a Bayesian alternative to deterministic audio embedding methods, focusing on transfer learning and uncertainty-aware variational inference. The core reasoning is to enhance model performance in downstream tasks by leveraging calibrated uncertainty information alongside knowledge from upstream tasks.