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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 76–100 of 267 papers

TitleStatusHype
Open-set Face Recognition with Neural Ensemble, Maximal Entropy Loss and Feature AugmentationCode0
Open-set Recognition based on the Combination of Deep Learning and Ensemble Method for Detecting Unknown Traffic ScenariosCode0
OpenMix+: Revisiting Data Augmentation for Open Set RecognitionCode0
A Survey of Text Classification Under Class Distribution ShiftCode0
OpenOOD: Benchmarking Generalized Out-of-Distribution DetectionCode0
OpenIncrement: A Unified Framework for Open Set Recognition and Deep Class-Incremental LearningCode0
Contracting Skeletal Kinematics for Human-Related Video Anomaly DetectionCode0
Accurate Open-set Recognition for Memory WorkloadCode0
Opening Deep Neural Networks with Generative ModelsCode0
FedOS: using open-set learning to stabilize training in federated learningCode0
AP18-OLR Challenge: Three Tasks and Their BaselinesCode0
Multi-Attribute Open Set RecognitionCode0
Non-Exhaustive Learning Using Gaussian Mixture Generative Adversarial NetworksCode0
Exploring the Open World Using Incremental Extreme Value MachinesCode0
CNS-Net: Conservative Novelty Synthesizing Network for Malware Recognition in an Open-set ScenarioCode0
Learning Unknowns from Unknowns: Diversified Negative Prototypes Generator for Few-Shot Open-Set RecognitionCode0
An Open-set Recognition and Few-Shot Learning Dataset for Audio Event Classification in Domestic EnvironmentsCode0
Cross-Rejective Open-Set SAR Image RegistrationCode0
LEGO-Learn: Label-Efficient Graph Open-Set LearningCode0
Classification-Reconstruction Learning for Open-Set RecognitionCode0
Learning a Neural-network-based Representation for Open Set RecognitionCode0
Mitigating Label Noise using Prompt-Based Hyperbolic Meta-Learning in Open-Set Domain GeneralizationCode0
Dynamic Against Dynamic: An Open-set Self-learning FrameworkCode0
Invisible Backdoor Attack with Dynamic Triggers against Person Re-identificationCode0
Domain Consensus Clustering for Universal Domain AdaptationCode0
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