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Open Set Learning
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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.
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이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Detecting Unknown Attacks in IoT Environments: An Open Set Classifier for Enhanced Network Intrusion Detection
LORD: Leveraging Open-Set Recognition with Unknown Data
Open-set Face Recognition with Neural Ensemble, Maximal Entropy Loss and Feature Augmentation
An Entropy-Awareness Meta-Learning Method for SAR Open-Set ATR
OpenGCD: Assisting Open World Recognition with Generalized Category Discovery
HomOpt: A Homotopy-Based Hyperparameter Optimization Method
Learning Large Margin Sparse Embeddings for Open Set Medical Diagnosis
Distill-SODA: Distilling Self-Supervised Vision Transformer for Source-Free Open-Set Domain Adaptation in Computational Pathology
Learning Adversarial Semantic Embeddings for Zero-Shot Recognition in Open Worlds
OpenAPMax: Abnormal Patterns-based Model for Real-World Alzheimer's Disease Diagnosis
OpenClinicalAI: An Open and Dynamic Model for Alzheimer's Disease Diagnosis
Towards Open Vocabulary Learning: A Survey
OpenNDD: Open Set Recognition for Neurodevelopmental Disorders Detection
Open-Set RF Fingerprinting via Improved Prototype Learning
Uncovering the Hidden Dynamics of Video Self-supervised Learning under Distribution Shifts
Few-Shot Open-Set Learning for On-Device Customization of KeyWord Spotting Systems
In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation
Learning for Transductive Threshold Calibration in Open-World Recognition
torchosr -- a PyTorch extension package for Open Set Recognition models evaluation in Python
CNS-Net: Conservative Novelty Synthesizing Network for Malware Recognition in an Open-set Scenario
MDENet: Multi-modal Dual-embedding Networks for Malware Open-set Recognition
Glocal Energy-based Learning for Few-Shot Open-Set Recognition
OpenMix+: Revisiting Data Augmentation for Open Set Recognition
Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification
Simple Domain Generalization Methods are Strong Baselines for Open Domain Generalization
On the link between generative semi-supervised learning and generative open-set recognition
Progressive Open Space Expansion for Open-Set Model Attribution
M-Tuning: Prompt Tuning with Mitigated Label Bias in Open-Set Scenarios
Open-World Object Detection via Discriminative Class Prototype Learning
Zero-Knowledge Zero-Shot Learning for Novel Visual Category Discovery
The Devil is in the Wrongly-classified Samples: Towards Unified Open-set Recognition
Improving Open-Set Semi-Supervised Learning with Self-Supervision
Contracting Skeletal Kinematics for Human-Related Video Anomaly Detection
Open-Set Likelihood Maximization for Few-Shot Learning
Subject-Independent Brain-Computer Interfaces with Open-Set Subject Recognition
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