SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–100 of 267 papers

TitleStatusHype
A Unified Benchmark for the Unknown Detection Capability of Deep Neural NetworksCode1
Conditional Variational Capsule Network for Open Set RecognitionCode1
A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future ChallengesCode1
HomOpt: A Homotopy-Based Hyperparameter Optimization MethodCode1
Adversarial Motorial Prototype Framework for Open Set RecognitionCode1
OneRing: A Simple Method for Source-free Open-partial Domain AdaptationCode1
DenseHybrid: Hybrid Anomaly Detection for Dense Open-set RecognitionCode1
BackMix: Regularizing Open Set Recognition by Removing Underlying Fore-Background PriorsCode1
Large-Scale Long-Tailed Recognition in an Open WorldCode1
Breaking with Fixed Set Pathology Recognition through Report-Guided Contrastive Training—0
An Adaptable Deep Learning-Based Intrusion Detection System to Zero-Day Attacks—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
All Beings Are Equal in Open Set Recognition—0
Denoising Autoencoders for Overgeneralization in Neural Networks—0
Deep Simplex Classifier for Maximizing the Margin in Both Euclidean and Angular Spaces—0
A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning—0
Advanced Vision Transformers and Open-Set Learning for Robust Mosquito Classification: A Novel Approach to Entomological Studies—0
Latent Cognizance: What Machine Really Learns—0
Deep Open-Set Recognition for Silicon Wafer Production Monitoring—0
Alignment Based Mathching Networks for One-Shot Classification and Open-Set Recognition—0
Deep Learning Approaches for Open Set Wireless Transmitter Authorization—0
Deep Learning and Open Set Malware Classification: A Survey—0
Knowledge is Never Enough: Towards Web Aided Deep Open World Recognition—0
Deep Compact Polyhedral Conic Classifier for Open and Closed Set Recognition—0
A Survey on Open Set Recognition—0
An In-Depth Study on Open-Set Camera Model Identification—0
A Survey on Open-Set Image Recognition—0
Data-Driven Hierarchical Open Set Recognition—0
Alignment Based Matching Networks for One-Shot Classification and Open-Set Recognition—0
Implicit supervision for fault detection and segmentation of emerging fault types with Deep Variational Autoencoders—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
Adversarial Robustness: Softmax versus Openmax—0
FSOS-AMC: Few-Shot Open-Set Learning for Automatic Modulation Classification—0
Informed Mixing -- Improving Open Set Recognition via Attribution-based Augmentation—0
From Coarse to Fine-Grained Open-Set Recognition—0
Few-shot Open-set Recognition Using Background as Unknowns—0
Are Out-of-Distribution Detection Methods Effective on Large-Scale Datasets?—0
Informed Decision-Making through Advancements in Open Set Recognition and Unknown Sample Detection—0
FedPD: Federated Open Set Recognition with Parameter Disentanglement—0
Gallery-Aware Uncertainty Estimation For Open-Set Face Recognition—0
Feature-Space Semantic Invariance: Enhanced OOD Detection for Open-Set Domain Generalization—0
A novel network training approach for open set image recognition—0
Generalizing Cross Entropy Loss with a Beta Proper Composite Loss: An Improved Loss Function for Open Set Recognition—0
Generative-Discriminative Feature Representations for Open-Set Recognition—0
ID-Conditioned Auto-Encoder for Unsupervised Anomaly Detection—0
Feature Decoupling in Self-supervised Representation Learning for Open Set Recognition—0
Familiarity-Based Open-Set Recognition Under Adversarial Attacks—0
Show:102550
← PrevPage 2 of 6Next →

No leaderboard results yet.