VI-PANN: Harnessing Transfer Learning and Uncertainty-Aware Variational Inference for Improved Generalization in Audio Pattern Recognition
Transfer learning (TL) is an increasingly popular approach to training deep learning (DL) models that leverages the knowledge gained by training a foundation model on diverse, large-scale datasets for use on downstream tasks where less domain- or task-specific data is available. The literature is ri...
Main Authors: | John Fischer, Marko Orescanin, Eric Eckstrand |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2024-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10456907/ |
Similar Items
-
General-Purpose Bayesian Tensor Learning With Automatic Rank Determination and Uncertainty Quantification
by: Kaiqi Zhang, et al.
Published: (2022-01-01) -
Parameter Uncertainty Quantification in an Idealized GCM With a Seasonal Cycle
by: Michael F. Howland, et al.
Published: (2022-03-01) -
Uncertainty Quantification in Classifying Complex Geological Facies Using Bayesian Deep Learning
by: Touhid Mohammad Hossain, et al.
Published: (2022-01-01) -
Bayesian losses for homoscedastic aleatoric uncertainty modeling in pollen image detection
by: Natalia E. Khanzhina
Published: (2021-08-01) -
Looking at the posterior: accuracy and uncertainty of neural-network predictions
by: Hampus Linander, et al.
Published: (2023-01-01)