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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

Showing 76100 of 267 papers

TitleStatusHype
Latent Space Energy-based Model for Fine-grained Open Set Recognition0
Detecting Unknown Attacks in IoT Environments: An Open Set Classifier for Enhanced Network Intrusion Detection0
LORD: Leveraging Open-Set Recognition with Unknown Data0
Open-set Face Recognition with Neural Ensemble, Maximal Entropy Loss and Feature AugmentationCode0
An Entropy-Awareness Meta-Learning Method for SAR Open-Set ATR0
OpenGCD: Assisting Open World Recognition with Generalized Category DiscoveryCode1
HomOpt: A Homotopy-Based Hyperparameter Optimization MethodCode1
Distill-SODA: Distilling Self-Supervised Vision Transformer for Source-Free Open-Set Domain Adaptation in Computational PathologyCode1
Learning Large Margin Sparse Embeddings for Open Set Medical Diagnosis0
Learning Adversarial Semantic Embeddings for Zero-Shot Recognition in Open WorldsCode1
OpenClinicalAI: An Open and Dynamic Model for Alzheimer's Disease Diagnosis0
OpenAPMax: Abnormal Patterns-based Model for Real-World Alzheimer's Disease Diagnosis0
OpenNDD: Open Set Recognition for Neurodevelopmental Disorders Detection0
Towards Open Vocabulary Learning: A SurveyCode2
Open-Set RF Fingerprinting via Improved Prototype Learning0
Uncovering the Hidden Dynamics of Video Self-supervised Learning under Distribution ShiftsCode1
Few-Shot Open-Set Learning for On-Device Customization of KeyWord Spotting SystemsCode1
In or Out? Fixing ImageNet Out-of-Distribution Detection EvaluationCode1
Learning for Transductive Threshold Calibration in Open-World Recognition0
torchosr -- a PyTorch extension package for Open Set Recognition models evaluation in PythonCode1
MDENet: Multi-modal Dual-embedding Networks for Malware Open-set Recognition0
CNS-Net: Conservative Novelty Synthesizing Network for Malware Recognition in an Open-set ScenarioCode0
Glocal Energy-based Learning for Few-Shot Open-Set RecognitionCode1
OpenMix+: Revisiting Data Augmentation for Open Set RecognitionCode0
Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification0
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