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Found 7 papers, 6 papers with code
In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation
The OOD detection performance when the in-distribution (ID) is ImageNet-1K is commonly being tested on a small range of test OOD datasets.
Ranked #1 on
Out-of-Distribution Detection
on ImageNet-1k vs NINCO
(using extra training data)
Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities
Moreover, we show that the confidence loss which is used by Outlier Exposure has an implicit scoring function which differs in a non-trivial fashion from the theoretically optimal scoring function in the case where training and test out-distribution are the same, which again is similar to the one used when training an Energy-Based OOD detector or when adding a background class.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Revisiting Out-of-Distribution Detection: A Simple Baseline is Surprisingly Effective
When trained in a shared fashion with a standard classifier, this binary discriminator reaches an OOD detection performance similar to that of Outlier Exposure.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Provably Robust Detection of Out-of-distribution Data (almost) for free
The application of machine learning in safety-critical systems requires a reliable assessment of uncertainty.
Certifiably Adversarially Robust Detection of Out-of-Distribution Data
Deep neural networks are known to be overconfident when applied to out-of-distribution (OOD) inputs which clearly do not belong to any class.
A simple way to make neural networks robust against diverse image corruptions
The human visual system is remarkably robust against a wide range of naturally occurring variations and corruptions like rain or snow.
Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem
We show that this technique is surprisingly effective in reducing the confidence of predictions far away from the training data while maintaining high confidence predictions and test error on the original classification task compared to standard training.
