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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
GlanceNets: Interpretabile, Leak-proof Concept-based ModelsCode1
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
Open Set Recognition using Vision Transformer with an Additional Detection HeadCode1
openFEAT: Improving Speaker Identification by Open-set Few-shot Embedding Adaptation with Transformer—0
Open-set Adversarial Defense with Clean-Adversarial Mutual LearningCode1
Open-Set Recognition of Breast Cancer Treatments—0
A Unified Benchmark for the Unknown Detection Capability of Deep Neural NetworksCode1
Synthetic Unknown Class Learning for Learning Unknowns—0
Latent Cognizance: What Machine Really Learns—0
A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future ChallengesCode1
Spatial Location Constraint Prototype Loss for Open Set RecognitionCode1
Evaluation of Various Open-Set Medical Imaging Tasks with Deep Neural Networks—0
Generalized Out-of-Distribution Detection: A SurveyCode1
Open-Set Recognition: a Good Closed-Set Classifier is All You Need?Code1
Generalizing Cross Entropy Loss with a Beta Proper Composite Loss: An Improved Loss Function for Open Set Recognition—0
Distance-Based Background Class Regularization 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
Evidential Deep Learning for Open Set Action RecognitionCode1
Adversarial Motorial Prototype Framework for Open Set RecognitionCode1
Learning Bounds for Open-Set LearningCode1
Non-Exhaustive Learning Using Gaussian Mixture Generative Adversarial NetworksCode0
Domain Consensus Clustering for Universal Domain AdaptationCode0
Self-supervised Detransformation Autoencoder for Representation Learning in Open Set Recognition—0
Opening Deep Neural Networks with Generative ModelsCode0
Open-set Recognition based on the Combination of Deep Learning and Ensemble Method for Detecting Unknown Traffic ScenariosCode0
Towards Novel Target Discovery Through Open-Set Domain AdaptationCode0
Conditional Variational Capsule Network for Open Set RecognitionCode1
OpenGAN: Open-Set Recognition via Open Data GenerationCode1
Learning Placeholders for Open-Set RecognitionCode1
Teacher-Explorer-Student Learning: A Novel Learning Method for Open Set Recognition—0
Collective Decision of One-vs-Rest Networks for Open Set Recognition—0
Hierarchical Self Attention Based Autoencoder for Open-Set Human Activity RecognitionCode1
Few-shot Open-set Recognition by Transformation ConsistencyCode1
Counterfactual Zero-Shot and Open-Set Visual RecognitionCode1
Adversarial Reciprocal Points Learning for Open Set RecognitionCode1
Deep Compact Polyhedral Conic Classifier for Open and Closed Set Recognition—0
Dense outlier detection and open-set recognition based on training with noisy negative images—0
An Empirical Exploration of Open-Set Recognition via Lightweight Statistical Pipelines—0
Task-Adaptive Negative Envision for Few-Shot Open-Set RecognitionCode1
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