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Found 7 papers, 0 papers with code
A Novel Unsupervised Post-Processing Calibration Method for DNNS with Robustness to Domain Shift
The uncertainty estimation is critical in real-world decision making applications, especially when distributional shift between the training and test data are prevalent.
Unsupervised Temperature Scaling: Robust Post-processing Calibration for Domain Shift
The uncertainty estimation is critical in real-world decision making applications, especially when distributional shift between the training and test data are prevalent.
Controlling Over-generalization and its Effect on Adversarial Examples Detection and Generation
As an appropriate training set for the extra class, we introduce two resources that are computationally efficient to obtain: a representative natural out-distribution set and interpolated in-distribution samples.
Unsupervised Temperature Scaling: An Unsupervised Post-Processing Calibration Method of Deep Networks
The great performances of deep learning are undeniable, with impressive results over a wide range of tasks.
Attended Temperature Scaling: A Practical Approach for Calibrating Deep Neural Networks
Temperature Scaling (TS) is a state-of-the-art among measure-based calibration methods which has low time and memory complexity as well as effectiveness.
Controlling Over-generalization and its Effect on Adversarial Examples Generation and Detection
As an appropriate training set for the extra class, we introduce two resources that are computationally efficient to obtain: a representative natural out-distribution set and interpolated in-distribution samples.
Diversity regularization in deep ensembles
Calibrating the confidence of supervised learning models is important for a variety of contexts where the certainty over predictions should be reliable.
