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Pytorch Ood ⭐ 166

👽 Out-of-Distribution Detection with PyTorch

[ECCV'22 Oral] Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes

Official PyTorch implementation of MOOD series: (1) MOODv1: Rethinking Out-of-distributionDetection: Masked Image Modeling Is All You Need. (2) MOODv2: Masked Image Modeling for Out-of-Distribution Detection.

Entropic Out Of Distribution Detection ⭐ 47

Add scalable state-of-the-art out-of-distribution detection (open set recognition) support by changing two lines of code! Perform efficient inferences (i.e., do not increase inference time) and detection without classification accuracy drop, hyperparameter tuning, or collecting additional data.

The official implementation for Diffusion Denoising Process for Perceptron Bias in Out-of-distribution Detection (DiffOOD)

Distinction Maximization Loss ⭐ 34

Improve out-of-distribution detection (open set recognition) and uncertainty estimation by changing a few lines of code in your project! Perform efficient inferences (i.e., do not increase inference time) without repetitive model training, hyperparameter tuning, or collecting additional data.

Pnml_ood_detection ⭐ 23

Out-of-distribution detection using the pNML regret. NeurIPS2021

Diffguard ⭐ 13

[ICCV 2023] The official implementation of paper "DiffGuard: Semantic Mismatch-Guided Out-of-Distribution Detection using Pre-trained Diffusion Models"

Robust Deep Learning ⭐ 9

Train your model from scratch or fine-tune a pretrained model using the losses provided in this library to improve out-of-distribution detection and uncertainty estimation performances. Calibrate your model to produce enhanced uncertainty estimations. Detect out-of-distribution data using the defined score type and threshold.

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