awesome_opensetrecognition_list
github.com/icgy96/awesome_opensetrecognition_list ↗A curated list of papers & resources linked to open set recognition, out-of-distribution, open set domain adaptation and open world recognition
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GitHub Stars
205
Curated Resources
8
Categories
6 hours ago
Last Refreshed
Tutorial & surveyOpen World VisionOpen Set RecognitionOpen Set Learning TheoryOut-of-DistributionAnomaly DetectionOpen Set Domain AdaptationOpen World Recognition
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Out-of-Distribution
- A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
- Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domain
- A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
- Background Data Resampling for Outlier-Aware Classification
- BaCOUn: Bayesian Classifers with Out-of-Distribution Uncertainty
- Can Subnetwork Structure be the Key to Out-of-Distribution Generalization?
Anomaly Detection
Open Set Recognition
- A bounded neural network for open set recognitionDeep Neural Network-based
- Adversarial Motorial Prototype Framework for Open Set RecognitionDeep Neural Network-based
- Adversarial Reciprocal Points Learning for Open Set RecognitionDeep Neural Network-based
- Adversarial Robustness: Softmax versus OpenmaxDeep Neural Network-based
- Alignment Based Matching Networks for One-Shot Classification and Open-Set RecognitionDeep Neural Network-based
- Best fitting hyperplanes for classificationTraditional Machine Learning Methods-based
Open Set Domain Adaptation
- Adversarial Network with Multiple Classifiers for Open Set Domain Adaptation
- Attract or Distract: Exploit the Margin of Open Set
- Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain Adaptation
- Collaborative Training of Balanced Random Forests for Open Set Domain Adaptation
- Exploring Category-Agnostic Clusters for Open-Set Domain Adaptation
- Known-class Aware Self-ensemble for Open Set Domain Adaptation
Open World Recognition
- A Unified Objective for Novel Class Discovery
- Automatically discovering and learning new visual categories with ranking statistics
- Learning and the Unknown: Surveying Steps Toward Open World Recognition
- Learning Cumulatively to Become More Knowledgeable
- Learning to Accept New Classes without Training
- Learning to discover novel visual categories via deep transfer clustering
Tutorial & survey
Open Set Learning Theory
Showing a sample of 205 resources. View the full list on GitHub →