Papers Open Set Learning
“Open Set Learning” 태그가 달린 논문 267편 · 필터 해제
Open-Set Automatic Target Recognition
Automatic Target Recognition (ATR) is a category of computer vision algorithms which attempts to recognize targets on data obtained from different sensors. ATR algorithms are extensively used in real-world scenarios such…
open-set classificationOpen Set LearningOpenAUC: Towards AUC-Oriented Open-Set Recognition
Traditional machine learning follows a close-set assumption that the training and test set share the same label space. While in many practical scenarios, it is inevitable that some test samples belong to unknown classes …
Novelty DetectionOpen Set LearningOpenOOD: Benchmarking Generalized Out-of-Distribution Detection
Out-of-distribution (OOD) detection is vital to safety-critical machine learning applications and has thus been extensively studied, with a plethora of methods developed in the literature. However, the field currently la…
Anomaly DetectionBenchmarkingOpen Set LearningOut-of-Distribution Detection+1Feature Decoupling in Self-supervised Representation Learning for Open Set Recognition
Assuming unknown classes could be present during classification, the open set recognition (OSR) task aims to classify an instance into a known class or reject it as unknown. In this paper, we use a two-stage training str…
Open Set LearningRepresentation LearningUnderstanding Open-Set Recognition by Jacobian Norm and Inter-Class Separation
The findings on open-set recognition (OSR) show that models trained on classification datasets are capable of detecting unknown classes not encountered during the training process. Specifically, after training, the learn…
Metric LearningOpen Set LearningOpen Set Recognition For Music Genre Classification
We explore segmentation of known and unknown genre classes using the open source GTZAN and FMA datasets. For each, we begin with best-case closed set genre classification, then we apply open set recognition methods. We o…
ClassificationGenre classificationMusic Genre ClassificationOpen Set LearningMeasuring Human Perception to Improve Open Set Recognition
The human ability to recognize when an object belongs or does not belong to a particular vision task outperforms all open set recognition algorithms. Human perception as measured by the methods and procedures of visual p…
Object RecognitionOpen Set LearningDeep Open-Set Recognition for Silicon Wafer Production Monitoring
The chips contained in any electronic device are manufactured over circular silicon wafers, which are monitored by inspection machines at different production stages. Inspection machines detect and locate any defect with…
Open Set LearningFedOS: using open-set learning to stabilize training in federated learning
Federated Learning is a recent approach to train statistical models on distributed datasets without violating privacy constraints. The data locality principle is preserved by sharing the model instead of the data between…
Federated LearningOpen Set LearningOpen Long-Tailed Recognition in a Dynamic World
Real world data often exhibits a long-tailed and open-ended (with unseen classes) distribution. A practical recognition system must balance between majority (head) and minority (tail) classes, generalize across the distr…
Active LearningClassificationFairnessFew-Shot Learning+2Multi-Attribute Open Set Recognition
Open Set Recognition (OSR) extends image classification to an open-world setting, by simultaneously classifying known classes and identifying unknown ones. While conventional OSR approaches can detect Out-of-Distribution…
Attributeimage-classificationImage ClassificationOpen Set LearningEight Years of Face Recognition Research: Reproducibility, Achievements and Open Issues
Automatic face recognition is a research area with high popularity. Many different face recognition algorithms have been proposed in the last thirty years of intensive research in the field. With the popularity of deep l…
Face RecognitionOpen Set Learning3DOS: Towards 3D Open Set Learning -- Benchmarking and Understanding Semantic Novelty Detection on Point Clouds
In recent years there has been significant progress in the field of 3D learning on classification, detection and segmentation problems. The vast majority of the existing studies focus on canonical closed-set conditions, …
BenchmarkingNovelty DetectionOpen Set LearningTowards Accurate Open-Set Recognition via Background-Class Regularization
In open-set recognition (OSR), classifiers should be able to reject unknown-class samples while maintaining high closed-set classification accuracy. To effectively solve the OSR problem, previous studies attempted to lim…
Open Set LearningDifficulty-Aware Simulator for Open Set Recognition
Open set recognition (OSR) assumes unknown instances appear out of the blue at the inference time. The main challenge of OSR is that the response of models for unknowns is totally unpredictable. Furthermore, the diversit…
DiversityGenerative Adversarial NetworkOpen Set LearningRethinking Few-Shot Class-Incremental Learning with Open-Set Hypothesis in Hyperbolic Geometry
Few-Shot Class-Incremental Learning (FSCIL) aims at incrementally learning novel classes from a few labeled samples by avoiding the overfitting and catastrophic forgetting simultaneously. The current protocol of FSCIL is…
class-incremental learningClass Incremental LearningFew-Shot Class-Incremental LearningIncremental Learning+2Rectifying Open-set Object Detection: A Taxonomy, Practical Applications, and Proper Evaluation
Open-set object detection (OSOD) has recently gained attention. It is to detect unknown objects while correctly detecting known objects. In this paper, we first point out that the recent studies' formalization of OSOD, w…
image-classificationImage ClassificationObjectobject-detection+3Few-shot Open-set Recognition Using Background as Unknowns
Few-shot open-set recognition aims to classify both seen and novel images given only limited training data of seen classes. The challenge of this task is that the model is required not only to learn a discriminative clas…
Open Set LearningPlex: Towards Reliability using Pretrained Large Model Extensions
A recent trend in artificial intelligence is the use of pretrained models for language and vision tasks, which have achieved extraordinary performance but also puzzling failures. Probing these models' abilities in divers…
Active LearningDecision MakingmodelOpen Set LearningOrthogonal-Coding-Based Feature Generation for Transductive Open-Set Recognition via Dual-Space Consistent Sampling
Open-set recognition (OSR) aims to simultaneously detect unknown-class samples and classify known-class samples. Most of the existing OSR methods are inductive methods, which generally suffer from the domain shift proble…
Open Set LearningTransductive Learning