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Found 5 papers, 3 papers with code
Robustness to Spurious Correlations Improves Semantic Out-of-Distribution Detection
The detection of shared-nuisance out-of-distribution (SN-OOD) inputs is particularly relevant in real-world applications, as anomalies and in-distribution inputs tend to be captured in the same settings during deployment.
Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets
However, we show that existing permutation invariant architectures, Deep Sets and Set Transformer, can suffer from vanishing or exploding gradients when they are deep.
Understanding Failures in Out-of-Distribution Detection with Deep Generative Models
Deep generative models (DGMs) seem a natural fit for detecting out-of-distribution (OOD) inputs, but such models have been shown to assign higher probabilities or densities to OOD images than images from the training distribution.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Out-of-distribution Generalization in the Presence of Nuisance-Induced Spurious Correlations
NURD finds a representation from this set that is most informative of the label under the nuisance-randomized distribution, and we prove that this representation achieves the highest performance regardless of the nuisance-label relationship.
Rapid Model Comparison by Amortizing Across Models
Comparing the inferences of diverse candidate models is an essential part of model checking and escaping local optima.
