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Found 5 papers, 2 papers with code
Unveiling Empirical Pathologies of Laplace Approximation for Uncertainty Estimation
In this paper, we critically evaluate Bayesian methods for uncertainty estimation in deep learning, focusing on the widely applied Laplace approximation and its variants.
Diversifying Deep Ensembles: A Saliency Map Approach for Enhanced OOD Detection, Calibration, and Accuracy
Deep ensembles are capable of achieving state-of-the-art results in classification and out-of-distribution (OOD) detection.
Deep Image Retrieval is not Robust to Label Noise
Large-scale datasets are essential for the success of deep learning in image retrieval.
EXACT: How to Train Your Accuracy
Classification tasks are usually evaluated in terms of accuracy.
Ranked #2 on
Image Classification
on SVHN
(Percentage correct metric)
Probabilistic Embeddings Revisited
We thus provide a new confidence evaluation benchmark and establish a baseline for future confidence prediction research.
