SOTAVerified

Open Set Learning

Traditional supervised learning aims to train a classifier in the closed-set world, where training and test samples share the same label space. Open set learning (OSL) is a more challenging and realistic setting, where there exist test samples from the classes that are unseen during training. Open set recognition (OSR) is the sub-task of detecting test samples which do not come from the training.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 191–200 of 267 papers

TitleStatusHype
Teacher-Explorer-Student Learning: A Novel Learning Method for Open Set Recognition—0
Collective Decision of One-vs-Rest Networks for Open Set Recognition—0
Hierarchical Self Attention Based Autoencoder for Open-Set Human Activity RecognitionCode1
Few-shot Open-set Recognition by Transformation ConsistencyCode1
Counterfactual Zero-Shot and Open-Set Visual RecognitionCode1
Adversarial Reciprocal Points Learning for Open Set RecognitionCode1
Deep Compact Polyhedral Conic Classifier for Open and Closed Set Recognition—0
Dense outlier detection and open-set recognition based on training with noisy negative images—0
An Empirical Exploration of Open-Set Recognition via Lightweight Statistical Pipelines—0
Task-Adaptive Negative Envision for Few-Shot Open-Set RecognitionCode1
Show:102550
← PrevPage 20 of 27Next →

No leaderboard results yet.