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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 426–450 of 629 papers

TitleStatusHype
Self-Supervised Anomaly Detection by Self-Distillation and Negative SamplingCode0
iDECODe: In-distribution Equivariance for Conformal Out-of-distribution Detection—0
Deep Hybrid Models for Out-of-Distribution Detection—0
Boundary Aware Learning for Out-of-distribution Detection—0
Energy-bounded Learning for Robust Models of Code—0
WOOD: Wasserstein-based Out-of-Distribution DetectionCode1
Hyperdimensional Feature Fusion for Out-Of-Distribution DetectionCode1
Benchmark for Out-of-Distribution Detection in Deep Reinforcement Learning—0
Provable Guarantees for Understanding Out-of-distribution DetectionCode1
Decomposing Representations for Deterministic Uncertainty Estimation—0
STEP: Out-of-Distribution Detection in the Presence of Limited In-Distribution Labeled Data—0
Locally Most Powerful Bayesian Test for Out-of-Distribution Detection using Deep Generative Models—0
FROB: Few-shot ROBust Model for Classification and Out-of-Distribution Detection—0
Data Invariants to Understand Unsupervised Out-of-Distribution Detection—0
DICE: Leveraging Sparsification for Out-of-Distribution DetectionCode1
Trustworthy Long-Tailed ClassificationCode1
Unsupervised Approaches for Out-Of-Distribution Dermoscopic Lesion Detection—0
Out of distribution detection for skin and malaria images—0
kFolden: k-Fold Ensemble for Out-Of-Distribution Detection—0
Detecting Compositionally Out-of-Distribution Examples in Semantic Parsing—0
PnPOOD : Out-Of-Distribution Detection for Text Classification via Plug andPlay Data Augmentation—0
CVAD: A generic medical anomaly detector based on Cascade VAECode0
GalilAI: Out-of-Task Distribution Detection using Causal Active Experimentation for Safe Transfer RL—0
Class-wise Thresholding for Robust Out-of-Distribution Detection—0
Exploring Covariate and Concept Shift for Detection and Calibration of Out-of-Distribution Data—0
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