Papers Open Set Learning
“Open Set Learning” 태그가 달린 논문 267편 · 필터 해제
Open-set Adversarial Defense with Clean-Adversarial Mutual Learning
Open-set recognition and adversarial defense study two key aspects of deep learning that are vital for real-world deployment. The objective of open-set recognition is to identify samples from open-set classes during test…
Adversarial DefenseDenoisingimage-classificationImage Classification+1Open-Set Recognition of Breast Cancer Treatments
Open-set recognition generalizes a classification task by classifying test samples as one of the known classes from training or "unknown." As novel cancer drug cocktails with improved treatment are continually discovered…
Open Set LearningRobust classificationA Unified Benchmark for the Unknown Detection Capability of Deep Neural Networks
Deep neural networks have achieved outstanding performance over various tasks, but they have a critical issue: over-confident predictions even for completely unknown samples. Many studies have been proposed to successful…
Open Set LearningOut-of-Distribution DetectionSynthetic Unknown Class Learning for Learning Unknowns
This paper addresses the open set recognition (OSR) problem, where the goal is to correctly classify samples of known classes while detecting unknown samples to reject. In the OSR problem, "unknown" is assumed to have in…
DiversityKnowledge DistillationOpen Set LearningLatent Cognizance: What Machine Really Learns
Despite overwhelming achievements in recognition accuracy, extending an open-set capability -- ability to identify when the question is out of scope -- remains greatly challenging in a scalable machine learning inference…
Object RecognitionOpen Set LearningSign Language RecognitionA Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges
Machine learning models often encounter samples that are diverged from the training distribution. Failure to recognize an out-of-distribution (OOD) sample, and consequently assign that sample to an in-class label signifi…
Anomaly DetectionNovelty DetectionOpen Set LearningOut-of-Distribution Detection+1Generalized Out-of-Distribution Detection: A Survey
Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of machine learning systems. For instance, in autonomous driving, we would like the driving system to issue an alert and hand over th…
Anomaly DetectionAutonomous DrivingNovelty DetectionOpen Set Learning+4Spatial Location Constraint Prototype Loss for Open Set Recognition
One of the challenges in pattern recognition is open set recognition. Compared with closed set recognition, open set recognition needs to reduce not only the empirical risk, but also the open space risk, and the reductio…
Open Set LearningEvaluation of Various Open-Set Medical Imaging Tasks with Deep Neural Networks
The current generation of deep neural networks has achieved close-to-human results on "closed-set" image recognition; that is, the classes being evaluated overlap with the training classes. Many recent methods attempt to…
Decision MakingOpen Set LearningOpen-Set Recognition: a Good Closed-Set Classifier is All You Need?
The ability to identify whether or not a test sample belongs to one of the semantic classes in a classifier's training set is critical to practical deployment of the model. This task is termed open-set recognition (OSR) …
AllOpen Set LearningOut-of-Distribution DetectionDistance-Based Background Class Regularization for Open-Set Recognition
In open-set recognition (OSR), classifiers should be able to reject unknown-class samples while maintaining robust closed-set classification performance. To solve the OSR problem based on pre-trained Softmax classifiers,…
Open Set LearningGeneralizing Cross Entropy Loss with a Beta Proper Composite Loss: An Improved Loss Function for Open Set Recognition
Open set recognition involves identifying data instances encountered during test time that do not belong to known classes in the training set. The majority of recent deep learning approaches to open set recognition use a…
Open Set LearningA novel network training approach for open set image recognition
Convolutional Neural Networks (CNNs) are commonly designed for closed set arrangements, where test instances only belong to some "Known Known" (KK) classes used in training. As such, they predict a class label for a test…
Data AugmentationOpen Set LearningRecognition Awareness: An Application of Latent Cognizance to Open-Set Recognition
This study investigates an application of a new probabilistic interpretation of a softmax output to Open-Set Recognition (OSR). Softmax is a mechanism wildly used in classification and object recognition. However, a soft…
ObjectObject RecognitionOpen Set LearningAn Adaptable Deep Learning-Based Intrusion Detection System to Zero-Day Attacks
The intrusion detection system (IDS) is an essential element of security monitoring in computer networks. An IDS distinguishes the malicious traffic from the benign one and determines the attack types targeting the asset…
ClusteringIntrusion DetectionOpen Set LearningA Survey on Open Set Recognition
Open Set Recognition (OSR) is about dealing with unknown situations that were not learned by the models during training. In this paper, we provide a survey of existing works about OSR and distinguish their respective adv…
Multi-class ClassificationNovelty DetectionOpen Set LearningSurveyM2IOSR: Maximal Mutual Information Open Set Recognition
In this work, we aim to address the challenging task of open set recognition (OSR). Many recent OSR methods rely on auto-encoders to extract class-specific features by a reconstruction strategy, requiring the network to …
Open Set LearningReconstruction guided Meta-learning for Few Shot Open Set Recognition
In many applications, we are constrained to learn classifiers from very limited data (few-shot classification). The task becomes even more challenging if it is also required to identify samples from unknown categories (o…
ClassificationMeta-Learningopen-set classificationOpen Set LearningEvidential Deep Learning for Open Set Action Recognition
In a real-world scenario, human actions are typically out of the distribution from training data, which requires a model to both recognize the known actions and reject the unknown. Different from image data, video action…
Action RecognitionDeep LearningOpen Set Action RecognitionOpen Set Learning+1Adversarial Motorial Prototype Framework for Open Set Recognition
Open set recognition is designed to identify known classes and to reject unknown classes simultaneously. Specifically, identifying known classes and rejecting unknown classes correspond to reducing the empirical risk and…
Open Set Learning