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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 201–250 of 267 papers

TitleStatusHype
Dense open-set recognition with synthetic outliers generated by Real NVPCode0
Learning Open Set Network with Discriminative Reciprocal PointsCode1
Empowering Knowledge Distillation via Open Set Recognition for Robust 3D Point Cloud Classification—0
Driver Anomaly Detection: A Dataset and Contrastive Learning ApproachCode1
A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning—0
Open-set Adversarial DefenseCode1
Open Set Recognition with Conditional Probabilistic Generative Models—0
GDumb: A Simple Approach that Questions Our Progress in Continual LearningCode1
Representative-Discriminative Learning for Open-set Land Cover Classification of Satellite ImageryCode1
ID-Conditioned Auto-Encoder for Unsupervised Anomaly Detection—0
Deep Active Learning via Open Set RecognitionCode0
MMF: A loss extension for feature learning in open set recognitionCode0
Fully Convolutional Open Set SegmentationCode1
More Information Supervised Probabilistic Deep Face Embedding Learning—0
Open-Set Recognition with Gaussian Mixture Variational Autoencoders—0
Generative-Discriminative Feature Representations for Open-Set Recognition—0
Few-Shot Open-Set Recognition using Meta-LearningCode1
Open Set Wireless Transmitter Authorization: Deep Learning Approaches and Dataset Considerations—0
Boosting Deep Open World Recognition by Clustering—0
One-vs-Rest Network-based Deep Probability Model for Open Set Recognition—0
Deep Learning and Open Set Malware Classification: A Survey—0
Class Anchor Clustering: a Loss for Distance-based Open Set RecognitionCode1
Hybrid Models for Open Set Recognition—0
Conditional Gaussian Distribution Learning for Open Set RecognitionCode1
Hierarchical Models: Intrinsic Separability in High Dimensions—0
An Open-set Recognition and Few-Shot Learning Dataset for Audio Event Classification in Domestic EnvironmentsCode0
Deep Learning Approaches for Open Set Wireless Transmitter Authorization—0
Open-set learning with augmented categories by exploiting unlabelled data—0
Implicit supervision for fault detection and segmentation of emerging fault types with Deep Variational Autoencoders—0
Are Out-of-Distribution Detection Methods Effective on Large-Scale Datasets?—0
Face Detection on Surveillance Images—0
Open Set Medical Diagnosis—0
Open Set Recognition Through Deep Neural Network Uncertainty: Does Out-of-Distribution Detection Require Generative Classifiers?—0
Visual and Semantic Prototypes-Jointly Guided CNN for Generalized Zero-shot Learning—0
Primate Face Identification in the Wild—0
Knowledge is Never Enough: Towards Web Aided Deep Open World Recognition—0
Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set RecognitionCode0
P-ODN: Prototype based Open Deep Network for Open Set Recognition—0
Alignment Based Mathching Networks for One-Shot Classification and Open-Set Recognition—0
An In-Depth Study on Open-Set Camera Model Identification—0
Large-Scale Long-Tailed Recognition in an Open WorldCode1
C2AE: Class Conditioned Auto-Encoder for Open-set Recognition—0
Open-Set Recognition Using Intra-Class Splitting—0
Alignment Based Matching Networks for One-Shot Classification and Open-Set Recognition—0
Deep CNN-based Multi-task Learning for Open-Set RecognitionCode0
The Importance of Metric Learning for Robotic Vision: Open Set Recognition and Active Learning—0
Individual common dolphin identification via metric embedding learning—0
Classification-Reconstruction Learning for Open-Set RecognitionCode0
Recent Advances in Open Set Recognition: A Survey—0
Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension—0
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