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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 601–629 of 629 papers

TitleStatusHype
Distilling the Unknown to Unveil CertaintyCode0
ZClassifier: Temperature Tuning and Manifold Approximation via KL Divergence on Logit SpaceCode0
Unsupervised Hybrid framework for ANomaly Detection (HAND) -- applied to Screening MammogramCode0
Towards Maximizing the Representation Gap between In-Domain & Out-of-Distribution ExamplesCode0
Out-of-Domain Detection for Low-Resource Text Classification TasksCode0
Gated Information Bottleneck for Generalization in Sequential EnvironmentsCode0
An Algorithm for Out-Of-Distribution Attack to Neural Network EncoderCode0
From Global to Local: Multi-scale Out-of-distribution DetectionCode0
Towards Open-World Object-based Anomaly Detection via Self-Supervised Outlier SynthesisCode0
Fast Decision Boundary based Out-of-Distribution DetectorCode0
FA: Forced Prompt Learning of Vision-Language Models for Out-of-Distribution DetectionCode0
Conservative Prediction via Data-Driven Confidence MinimizationCode0
AdaSCALE: Adaptive Scaling for OOD DetectionCode0
Probing Contextual Diversity for Dense Out-of-Distribution DetectionCode0
ProHOC: Probabilistic Hierarchical Out-of-Distribution Classification via Multi-Depth NetworksCode0
ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain DetectionCode0
Evidential Spectrum-Aware Contrastive Learning for OOD Detection in Dynamic GraphsCode0
Enhancing Reconstruction-Based Out-of-Distribution Detection in Brain MRI with Model and Metric EnsemblesCode0
Enhancing Out-of-Distribution Detection in Medical Imaging with Normalizing FlowsCode0
Probabilistic Trust Intervals for Out of Distribution DetectionCode0
Towards Reliable AI Model Deployments: Multiple Input Mixup for Out-of-Distribution DetectionCode0
PViT: Prior-augmented Vision Transformer for Out-of-distribution DetectionCode0
Task-agnostic Out-of-Distribution Detection Using Kernel Density EstimationCode0
Unsupervised Out-of-Distribution Detection by Maximum Classifier DiscrepancyCode0
Unsupervised Out-of-Distribution Detection in Medical Imaging Using Multi-Exit Class Activation Maps and Feature MaskingCode0
Identity Curvature Laplace Approximation for Improved Out-of-Distribution DetectionCode0
Confidence-based Out-of-Distribution Detection: A Comparative Study and AnalysisCode0
Confidence-Aware and Self-Supervised Image Anomaly LocalisationCode0
Concept-based Explanations for Out-Of-Distribution DetectorsCode0
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