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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 151–200 of 267 papers

TitleStatusHype
Deep Simplex Classifier for Maximizing the Margin in Both Euclidean and Angular Spaces—0
Accurate Open-set Recognition for Memory WorkloadCode0
Spatial-Temporal Attention Network for Open-Set Fine-Grained Image Recognition—0
PartCom: Part Composition Learning for 3D Open-Set Recognition—0
MetaMax: Improved Open-Set Deep Neural Networks via Weibull Calibration—0
Invisible Backdoor Attack with Dynamic Triggers against Person Re-identificationCode0
OpenOOD: Benchmarking Generalized Out-of-Distribution DetectionCode0
Feature Decoupling in Self-supervised Representation Learning for Open Set Recognition—0
Understanding Open-Set Recognition by Jacobian Norm and Inter-Class Separation—0
Open Set Recognition For Music Genre Classification—0
Measuring Human Perception to Improve Open Set Recognition—0
Deep Open-Set Recognition for Silicon Wafer Production Monitoring—0
FedOS: using open-set learning to stabilize training in federated learningCode0
Open Long-Tailed Recognition in a Dynamic World—0
Multi-Attribute Open Set RecognitionCode0
Eight Years of Face Recognition Research: Reproducibility, Achievements and Open Issues—0
3DOS: Towards 3D Open Set Learning -- Benchmarking and Understanding Semantic Novelty Detection on Point CloudsCode0
Towards Accurate Open-Set Recognition via Background-Class Regularization—0
Rethinking Few-Shot Class-Incremental Learning with Open-Set Hypothesis in Hyperbolic Geometry—0
Rectifying Open-set Object Detection: A Taxonomy, Practical Applications, and Proper Evaluation—0
Few-shot Open-set Recognition Using Background as Unknowns—0
Plex: Towards Reliability using Pretrained Large Model ExtensionsCode0
Orthogonal-Coding-Based Feature Generation for Transductive Open-Set Recognition via Dual-Space Consistent Sampling—0
Know Your Space: Inlier and Outlier Construction for Calibrating Medical OOD Detectors—0
Class-Specific Semantic Reconstruction for Open Set Recognition—0
Dummy Prototypical Networks for Few-Shot Open-Set Keyword Spotting—0
Test Time Transform Prediction for Open Set Histopathological Image RecognitionCode0
Open-Set Recognition with Gradient-Based Representations—0
OOD Augmentation May Be at Odds with Open-Set Recognition—0
Exploring the Open World Using Incremental Extreme Value MachinesCode0
Evaluating Uncertainty Calibration for Open-Set Recognition—0
Breaking with Fixed Set Pathology Recognition through Report-Guided Contrastive Training—0
Representation learning with function call graph transformations for malware open set recognition—0
Prototype-based Domain Generalization Framework for Subject-Independent Brain-Computer Interfaces—0
Open-set Recognition via Augmentation-based Similarity Learning—0
PMAL: Open Set Recognition via Robust Prototype Mining—0
openFEAT: Improving Speaker Identification by Open-set Few-shot Embedding Adaptation with Transformer—0
Open-Set Recognition of Breast Cancer Treatments—0
Synthetic Unknown Class Learning for Learning Unknowns—0
Latent Cognizance: What Machine Really Learns—0
Evaluation of Various Open-Set Medical Imaging Tasks with Deep Neural Networks—0
Distance-Based Background Class Regularization for Open-Set Recognition—0
Generalizing Cross Entropy Loss with a Beta Proper Composite Loss: An Improved Loss Function for Open Set Recognition—0
A novel network training approach for open set image recognition—0
Recognition Awareness: An Application of Latent Cognizance to Open-Set Recognition—0
An Adaptable Deep Learning-Based Intrusion Detection System to Zero-Day Attacks—0
A Survey on Open Set Recognition—0
M2IOSR: Maximal Mutual Information Open Set Recognition—0
Reconstruction guided Meta-learning for Few Shot Open Set Recognition—0
Non-Exhaustive Learning Using Gaussian Mixture Generative Adversarial NetworksCode0
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