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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 101–125 of 267 papers

TitleStatusHype
Robustness-enhanced Myoelectric Control with GAN-based Open-set RecognitionCode0
Multi-Attribute Open Set RecognitionCode0
Opening Deep Neural Networks with Generative ModelsCode0
Dynamic Against Dynamic: An Open-set Self-learning FrameworkCode0
Mitigating Label Noise using Prompt-Based Hyperbolic Meta-Learning in Open-Set Domain GeneralizationCode0
SphOR: A Representation Learning Perspective on Open-set Recognition for Identifying Unknown Classes in Deep Learning ModelsCode0
Domain Consensus Clustering for Universal Domain AdaptationCode0
Improving Open-Set Semi-Supervised Learning with Self-SupervisionCode0
MMF: A loss extension for feature learning in open set recognitionCode0
Learning Unknowns from Unknowns: Diversified Negative Prototypes Generator for Few-Shot Open-Set RecognitionCode0
Advancing Image Retrieval with Few-Shot Learning and Relevance FeedbackCode0
LEGO-Learn: Label-Efficient Graph Open-Set LearningCode0
OpenMix+: Revisiting Data Augmentation for Open Set RecognitionCode0
Breaking with Fixed Set Pathology Recognition through Report-Guided Contrastive Training—0
Detecting Unknown Attacks in IoT Environments: An Open Set Classifier for Enhanced Network Intrusion Detection—0
Latent Cognizance: What Machine Really Learns—0
Dense outlier detection and open-set recognition based on training with noisy negative images—0
Boosting Deep Open World Recognition by Clustering—0
An Adaptable Deep Learning-Based Intrusion Detection System to Zero-Day Attacks—0
Know Yourself Better: Diverse Discriminative Feature Learning Improves Open Set Recognition—0
Latent Space Energy-based Model for Fine-grained Open Set Recognition—0
Knowledge is Never Enough: Towards Web Aided Deep Open World Recognition—0
Implicit supervision for fault detection and segmentation of emerging fault types with Deep Variational Autoencoders—0
Open-set learning with augmented categories by exploiting unlabelled data—0
Denoising Autoencoders for Overgeneralization in Neural Networks—0
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