SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 591–600 of 629 papers

TitleStatusHype
VisTa: Visual-contextual and Text-augmented Zero-shot Object-level OOD Detection—0
Instance-Aware Observer Network for Out-of-Distribution Object Segmentation—0
Interpretable Out-Of-Distribution Detection Using Pattern Identification—0
Interpreting deep learning output for out-of-distribution detection—0
Intra-class Mixup for Out-of-Distribution Detection—0
Adversarial Distributions Against Out-of-Distribution Detectors—0
Is it all a cluster game? -- Exploring Out-of-Distribution Detection based on Clustering in the Embedding Space—0
Is Out-of-Distribution Detection Learnable?—0
Tensor-Train Point Cloud Compression and Efficient Approximate Nearest-Neighbor Search—0
Joint Distribution across Representation Space for Out-of-Distribution Detection—0
Show:102550
← PrevPage 60 of 63Next →

No leaderboard results yet.