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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 501–525 of 629 papers

TitleStatusHype
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
Exploring Covariate and Concept Shift for Detection and Calibration of Out-of-Distribution Data—0
Class-wise Thresholding for Robust Out-of-Distribution Detection—0
Understanding the Role of Self-Supervised Learning in Out-of-Distribution Detection Task—0
Distributionally Robust Recurrent Decoders with Random Network Distillation—0
Natural Attribute-based Shift Detection—0
Identifying Incorrect Classifications with Balanced UncertaintyCode0
Meta Learning Low Rank Covariance Factors for Energy-Based Deterministic Uncertainty—0
On out-of-distribution detection with Bayesian neural networksCode0
Gated Information Bottleneck for Generalization in Sequential EnvironmentsCode0
Out-of-Distribution Detection for Medical Applications: Guidelines for Practical EvaluationCode0
Revisiting flow generative models for Out-of-distribution detection—0
Decomposing Texture and Semantics for Out-of-distribution Detection—0
Towards Unknown-aware Learning with Virtual Outlier Synthesis—0
Towards Unknown-aware Deep Q-Learning—0
MOG: Molecular Out-of-distribution Generation with Energy-based Models—0
Efficient Out-of-Distribution Detection via CVAE data Generation—0
Revisiting Out-of-Distribution Detection: A Simple Baseline is Surprisingly Effective—0
Intra-class Mixup for Out-of-Distribution Detection—0
No Shifted Augmentations (NSA): strong baselines for self-supervised Anomaly Detection—0
Adversarial Distributions Against Out-of-Distribution Detectors—0
Sneakoscope: Revisiting Unsupervised Out-of-Distribution Detection—0
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