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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 501–510 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
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