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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

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Showing 201–210 of 267 papers

TitleStatusHype
Dense open-set recognition with synthetic outliers generated by Real NVPCode0
Learning Open Set Network with Discriminative Reciprocal PointsCode1
Empowering Knowledge Distillation via Open Set Recognition for Robust 3D Point Cloud Classification—0
Driver Anomaly Detection: A Dataset and Contrastive Learning ApproachCode1
A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning—0
Open-set Adversarial DefenseCode1
Open Set Recognition with Conditional Probabilistic Generative Models—0
GDumb: A Simple Approach that Questions Our Progress in Continual LearningCode1
Representative-Discriminative Learning for Open-set Land Cover Classification of Satellite ImageryCode1
ID-Conditioned Auto-Encoder for Unsupervised Anomaly Detection—0
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