Out-of-Distribution Detection

The OOD module provides methods for detecting when a model encounters data that is significantly different from its training distribution.

Score-based Methods

MSP(model)	Maximum-Softmax-Probability (Hendrycks & Gimpel, 2017).
MaxLogit(model)	MaxLogit OOD detection (Hendrycks et al., 2019).
Energy(model[, temperature])	Energy-based score (Liu et al., NeurIPS 2020).
ODIN(model[, temperature, epsilon])	ODIN (Liang et al., ICLR 2018).

Distance-based Methods

Mahalanobis(model[, layer_name])	Feature-space Mahalanobis (Lee et al., NeurIPS 2018).
KNN(model[, k, layer_name])	KNN-based OOD detection (Sun et al., NeurIPS 2022).

Training Methods

mixup_data(x, y[, alpha])	Apply mixup data augmentation.
OutlierExposureLoss([lambda_oe])	Outlier Exposure (OE) for improved OOD detection.

Metrics

auroc(id_scores, ood_scores)	Compute AUROC for OOD detection.
fpr_at_tpr(id_scores, ood_scores[, tpr])	Compute False Positive Rate at a target True Positive Rate.

Visualization