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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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GlanceNets: Interpretabile, Leak-proof Concept-based Models
Exploring the Open World Using Incremental Extreme Value Machines
Evaluating Uncertainty Calibration for Open-Set Recognition
Breaking with Fixed Set Pathology Recognition through Report-Guided Contrastive Training
Representation learning with function call graph transformations for malware open set recognition
Prototype-based Domain Generalization Framework for Subject-Independent Brain-Computer Interfaces
Open-set Recognition via Augmentation-based Similarity Learning
Open Set Recognition using Vision Transformer with an Additional Detection Head
PMAL: Open Set Recognition via Robust Prototype Mining
openFEAT: Improving Speaker Identification by Open-set Few-shot Embedding Adaptation with Transformer
Open-set Adversarial Defense with Clean-Adversarial Mutual Learning
Open-Set Recognition of Breast Cancer Treatments
A Unified Benchmark for the Unknown Detection Capability of Deep Neural Networks
Synthetic Unknown Class Learning for Learning Unknowns
Latent Cognizance: What Machine Really Learns
A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges
Evaluation of Various Open-Set Medical Imaging Tasks with Deep Neural Networks
Spatial Location Constraint Prototype Loss for Open Set Recognition
Generalized Out-of-Distribution Detection: A Survey
Open-Set Recognition: a Good Closed-Set Classifier is All You Need?
A novel network training approach for open set image recognition
Recognition Awareness: An Application of Latent Cognizance to Open-Set Recognition
An Adaptable Deep Learning-Based Intrusion Detection System to Zero-Day Attacks
A Survey on Open Set Recognition
M2IOSR: Maximal Mutual Information Open Set Recognition
Reconstruction guided Meta-learning for Few Shot Open Set Recognition
Evidential Deep Learning for Open Set Action Recognition
Adversarial Motorial Prototype Framework for Open Set Recognition
Learning Bounds for Open-Set Learning
Non-Exhaustive Learning Using Gaussian Mixture Generative Adversarial Networks
Self-supervised Detransformation Autoencoder for Representation Learning in Open Set Recognition
Opening Deep Neural Networks with Generative Models
Open-set Recognition based on the Combination of Deep Learning and Ensemble Method for Detecting Unknown Traffic Scenarios
Towards Novel Target Discovery Through Open-Set Domain Adaptation
Conditional Variational Capsule Network for Open Set Recognition
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