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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 111–120 of 267 papers

TitleStatusHype
Learning a Neural-network-based Representation for Open Set RecognitionCode0
Advancing Image Retrieval with Few-Shot Learning and Relevance FeedbackCode0
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
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
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
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