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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 176–200 of 267 papers

TitleStatusHype
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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