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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.
Zero-X: A Blockchain-Enabled Open-Set Federated Learning Framework for Zero-Day Attack Detection in IoV
OpenSlot: Mixed Open-Set Recognition with Object-Centric Learning
Large-Scale Evaluation of Open-Set Image Classification Techniques
Cascading Unknown Detection with Known Classification for Open Set Recognition
Informed Decision-Making through Advancements in Open Set Recognition and Unknown Sample Detection
Revealing the Two Sides of Data Augmentation: An Asymmetric Distillation-based Win-Win Solution for Open-Set Recognition
Dynamic Against Dynamic: An Open-set Self-learning Framework
Open-Set Video-based Facial Expression Recognition with Human Expression-sensitive Prompting
Know Yourself Better: Diverse Discriminative Feature Learning Improves Open Set Recognition
Out-of-Distribution Data: An Acquaintance of Adversarial Examples -- A Survey
Open-Set Recognition in the Age of Vision-Language Models
ROG$_{PL}$: Robust Open-Set Graph Learning via Region-Based Prototype Learning
Taking Class Imbalance Into Account in Open Set Recognition Evaluation
All Beings Are Equal in Open Set Recognition
Open-Set Facial Expression Recognition
Exploring Diverse Representations for Open Set Recognition
A Survey on Open-Set Image Recognition
Class Information Guided Reconstruction for Automatic Modulation Open-Set Recognition
Advancing Image Retrieval with Few-Shot Learning and Relevance Feedback
Managing the unknown: a survey on Open Set Recognition and tangential areas
Navigating Open Set Scenarios for Skeleton-based Action Recognition
Towards Open-set Gesture Recognition via Feature Activation Enhancement and Orthogonal Prototype Learning
Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach
Unified Classification and Rejection: A One-versus-All Framework
EdgeFM: Leveraging Foundation Model for Open-set Learning on the Edge
Familiarity-Based Open-Set Recognition Under Adversarial Attacks
Analysis and Applications of Deep Learning with Finite Samples in Full Life-Cycle Intelligence of Nuclear Power Generation
Open-Set Object Recognition Using Mechanical Properties During Interaction
Open-Set Face Recognition with Maximal Entropy and Objectosphere Loss
MENTOR: Human Perception-Guided Pretraining for Increased Generalization
Open-Set Image Tagging with Multi-Grained Text Supervision
Pairwise Similarity Learning is SimPLE
OpenIncrement: A Unified Framework for Open Set Recognition and Deep Class-Incremental Learning
Recursive Counterfactual Deconfounding for Object Recognition
Domain Adaptive Few-Shot Open-Set Learning
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