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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 151–200 of 629 papers

TitleStatusHype
Uncertainty Aware Semi-Supervised Learning on Graph DataCode1
Dream the Impossible: Outlier Imagination with Diffusion ModelsCode1
A Simple Fix to Mahalanobis Distance for Improving Near-OOD DetectionCode1
Natural Posterior Network: Deep Bayesian Uncertainty for Exponential Family DistributionsCode1
A framework for benchmarking class-out-of-distribution detection and its application to ImageNetCode1
Neural Relation Graph: A Unified Framework for Identifying Label Noise and Outlier DataCode1
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
How Useful are Gradients for OOD Detection Really?—0
How Does Fine-Tuning Impact Out-of-Distribution Detection for Vision-Language Models?—0
An out-of-distribution discriminator based on Bayesian neural network epistemic uncertainty—0
Detecting Out-of-Distribution Examples with Gram Matrices—0
Boosting LLM-based Relevance Modeling with Distribution-Aware Robust Learning—0
How to Enable Uncertainty Estimation in Proximal Policy Optimization—0
Humble your Overconfident Networks: Unlearning Overfitting via Sequential Monte Carlo Tempered Deep Ensembles—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
Beyond Mahalanobis-Based Scores for Textual OOD Detection—0
Histogram- and Diffusion-Based Medical Out-of-Distribution Detection—0
Detecting and Learning Out-of-Distribution Data in the Open world: Algorithm and Theory—0
Density of States Estimation for Out-of-Distribution Detection—0
Benchmarking Post-Hoc Unknown-Category Detection in Food Recognition—0
VRA: Variational Rectified Activation for Out-of-distribution Detection—0
HOOD: Real-Time Human Presence and Out-of-Distribution Detection Using FMCW Radar—0
Hyperbolic Metric Learning for Visual Outlier Detection—0
Deep Neural Network Identification of Limnonectes Species and New Class Detection Using Image Data—0
Deep Metric Learning-Based Out-of-Distribution Detection with Synthetic Outlier Exposure—0
Benchmark for Out-of-Distribution Detection in Deep Reinforcement Learning—0
Deep Hybrid Models for Out-of-Distribution Detection—0
Harnessing Large Language and Vision-Language Models for Robust Out-of-Distribution Detection—0
Decomposing Texture and Semantics for Out-of-distribution Detection—0
Decomposing Representations for Deterministic Uncertainty Estimation—0
A deep learning framework for the detection and quantification of drusen and reticular pseudodrusen on optical coherence tomography—0
Beyond Perceptual Distances: Rethinking Disparity Assessment for Out-of-Distribution Detection with Diffusion Models—0
Data Invariants to Understand Unsupervised Out-of-Distribution Detection—0
Federated Learning with Uncertainty via Distilled Predictive Distributions—0
GROOD: Gradient-Aware Out-of-Distribution Detection—0
Batch-Ensemble Stochastic Neural Networks for Out-of-Distribution Detection—0
Curved Geometric Networks for Visual Anomaly Recognition—0
BAM: Box Abstraction Monitors for Real-time OoD Detection in Object Detection—0
Credal Wrapper of Model Averaging for Uncertainty Estimation in Classification—0
COOD: Combined out-of-distribution detection using multiple measures for anomaly & novel class detection in large-scale hierarchical classification—0
Graph Synthetic Out-of-Distribution Exposure with Large Language Models—0
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