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Out of Distribution (OOD) Detection

Out of Distribution (OOD) Detection is the task of detecting instances that do not belong to the distribution the classifier has been trained on. OOD data is often referred to as "unseen" data, as the model has not encountered it during training.

OOD detection is typically performed by training a model to distinguish between in-distribution (ID) data, which the model has seen during training, and OOD data, which it has not seen. This can be done using a variety of techniques, such as training a separate OOD detector, or modifying the model's architecture or loss function to make it more sensitive to OOD data.

Papers

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Showing 151–175 of 629 papers

TitleStatusHype
SAFE: Sensitivity-Aware Features for Out-of-Distribution Object DetectionCode1
Dream the Impossible: Outlier Imagination with Diffusion ModelsCode1
A Simple Fix to Mahalanobis Distance for Improving Near-OOD DetectionCode1
Secure On-Device Video OOD Detection Without BackpropagationCode1
A framework for benchmarking class-out-of-distribution detection and its application to ImageNetCode1
Multidimensional Uncertainty-Aware Evidential Neural NetworksCode1
EAT: Towards Long-Tailed Out-of-Distribution DetectionCode1
CLIPN for Zero-Shot OOD Detection: Teaching CLIP to Say NoCode1
AdaptiveMix: Improving GAN Training via Feature Space ShrinkageCode1
DICE: A Simple Sparsification Method for Out-of-distribution Detection—0
Detecting when pre-trained nnU-Net models fail silently for Covid-19 lung lesion segmentation—0
Boundary Aware Learning for Out-of-distribution Detection—0
Adversarial Distributions Against Out-of-Distribution Detectors—0
FROB: Few-shot ROBust Model for Classification with Out-of-Distribution Detection—0
FOOD: Facial Authentication and Out-of-Distribution Detection with Short-Range FMCW Radar—0
An out-of-distribution discriminator based on Bayesian neural network epistemic uncertainty—0
Fool Me Once: Robust Selective Segmentation via Out-of-Distribution Detection with Contrastive Learning—0
Detecting Out-of-Distribution Examples with Gram Matrices—0
Boosting LLM-based Relevance Modeling with Distribution-Aware Robust Learning—0
Free Lunch for Generating Effective Outlier Supervision—0
FROB: Few-shot ROBust Model for Classification and Out-of-Distribution Detection—0
Detecting Out-of-distribution Examples via Class-conditional Impressions Reappearing—0
A Closer Look at the Learnability of Out-of-Distribution (OOD) Detection—0
Detecting Compositionally Out-of-Distribution Examples in Semantic Parsing—0
Detecting Anomalous Event Sequences with Temporal Point Processes—0
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