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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 201–225 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
Deep Active Learning via Open Set RecognitionCode0
MMF: A loss extension for feature learning in open set recognitionCode0
Fully Convolutional Open Set SegmentationCode1
More Information Supervised Probabilistic Deep Face Embedding Learning—0
Open-Set Recognition with Gaussian Mixture Variational Autoencoders—0
Generative-Discriminative Feature Representations for Open-Set Recognition—0
Few-Shot Open-Set Recognition using Meta-LearningCode1
Open Set Wireless Transmitter Authorization: Deep Learning Approaches and Dataset Considerations—0
Boosting Deep Open World Recognition by Clustering—0
One-vs-Rest Network-based Deep Probability Model for Open Set Recognition—0
Deep Learning and Open Set Malware Classification: A Survey—0
Class Anchor Clustering: a Loss for Distance-based Open Set RecognitionCode1
Hybrid Models for Open Set Recognition—0
Conditional Gaussian Distribution Learning for Open Set RecognitionCode1
Hierarchical Models: Intrinsic Separability in High Dimensions—0
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