Papers with Code 아카이브
Open Set Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 0
- 보존된 평가 기록
- 0
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.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Deep Simplex Classifier for Maximizing the Margin in Both Euclidean and Angular Spaces
Accurate Open-set Recognition for Memory Workload
Spatial-Temporal Attention Network for Open-Set Fine-Grained Image Recognition
MetaMax: Improved Open-Set Deep Neural Networks via Weibull Calibration
PartCom: Part Composition Learning for 3D Open-Set Recognition
Invisible Backdoor Attack with Dynamic Triggers against Person Re-identification
Open-Set Automatic Target Recognition
OpenAUC: Towards AUC-Oriented Open-Set Recognition
OpenOOD: Benchmarking Generalized Out-of-Distribution Detection
Feature Decoupling in Self-supervised Representation Learning for Open Set Recognition
Understanding Open-Set Recognition by Jacobian Norm and Inter-Class Separation
Open Set Recognition For Music Genre Classification
Measuring Human Perception to Improve Open Set Recognition
Deep Open-Set Recognition for Silicon Wafer Production Monitoring
FedOS: using open-set learning to stabilize training in federated learning
Open Long-Tailed Recognition in a Dynamic World
Multi-Attribute Open Set Recognition
Eight Years of Face Recognition Research: Reproducibility, Achievements and Open Issues
3DOS: Towards 3D Open Set Learning -- Benchmarking and Understanding Semantic Novelty Detection on Point Clouds
Towards Accurate Open-Set Recognition via Background-Class Regularization
Rectifying Open-set Object Detection: A Taxonomy, Practical Applications, and Proper Evaluation
Rethinking Few-Shot Class-Incremental Learning with Open-Set Hypothesis in Hyperbolic Geometry
Difficulty-Aware Simulator for Open Set Recognition
Few-shot Open-set Recognition Using Background as Unknowns
Plex: Towards Reliability using Pretrained Large Model Extensions
Orthogonal-Coding-Based Feature Generation for Transductive Open-Set Recognition via Dual-Space Consistent Sampling
Know Your Space: Inlier and Outlier Construction for Calibrating Medical OOD Detectors
DenseHybrid: Hybrid Anomaly Detection for Dense Open-set Recognition
Class-Specific Semantic Reconstruction for Open Set Recognition
Dummy Prototypical Networks for Few-Shot Open-Set Keyword Spotting
Test Time Transform Prediction for Open Set Histopathological Image Recognition
Model-Agnostic Few-Shot Open-Set Recognition
Maximum Class Separation as Inductive Bias in One Matrix
Open-Set Recognition with Gradient-Based Representations
OOD Augmentation May Be at Odds with Open-Set Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.