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Found 4 papers, 1 papers with code
Understanding the Double Descent Phenomenon in Deep Learning
Combining empirical risk minimization with capacity control is a classical strategy in machine learning when trying to control the generalization gap and avoid overfitting, as the model class capacity gets larger.
Energy Correction Model in the Feature Space for Out-of-Distribution Detection
In this work, we study the out-of-distribution (OOD) detection problem through the use of the feature space of a pre-trained deep classifier.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Hybrid Energy Based Model in the Feature Space for Out-of-Distribution Detection
HEAT complements prior density estimators of the ID density, e. g. parametric models like the Gaussian Mixture Model (GMM), to provide an accurate yet robust density estimation.
Effective Uncertainty Estimation with Evidential Models for Open-World Recognition
A crucial property of KLoS is to be a class-wise divergence measure built from in-distribution samples and to not require OOD training data, in contrast to current second-order uncertainty measures.
