Papers Open Set Learning
“Open Set Learning” 태그가 달린 논문 267편 · 필터 해제
Deep Learning and Open Set Malware Classification: A Survey
As the Internet is growing rapidly these years, the variant of malicious software, which often referred to as malware, has become one of the major and serious threats to Internet users. The dramatic increase of malware h…
BIG-bench Machine LearningClassificationDeep LearningGeneral Classification+3Class Anchor Clustering: a Loss for Distance-based Open Set Recognition
In open set recognition, deep neural networks encounter object classes that were unknown during training. Existing open set classifiers distinguish between known and unknown classes by measuring distance in a network's l…
ClusteringOpen Set LearningHybrid Models for Open Set Recognition
Open set recognition requires a classifier to detect samples not belonging to any of the classes in its training set. Existing methods fit a probability distribution to the training samples on their embedding space and d…
Open Set LearningOut-of-Distribution DetectionConditional Gaussian Distribution Learning for Open Set Recognition
Deep neural networks have achieved state-of-the-art performance in a wide range of recognition/classification tasks. However, when applying deep learning to real-world applications, there are still multiple challenges. A…
General ClassificationOpen Set LearningHierarchical Models: Intrinsic Separability in High Dimensions
It has long been noticed that high dimension data exhibits strange patterns. This has been variously interpreted as either a "blessing" or a "curse", causing uncomfortable inconsistencies in the literature. We propose th…
BIG-bench Machine LearningOpen Set LearningVocal Bursts Intensity PredictionAn Open-set Recognition and Few-Shot Learning Dataset for Audio Event Classification in Domestic Environments
The problem of training with a small set of positive samples is known as few-shot learning (FSL). It is widely known that traditional deep learning (DL) algorithms usually show very good performance when trained with lar…
Face RecognitionFew-Shot LearningGeneral ClassificationOpen Set Learning+2Deep Learning Approaches for Open Set Wireless Transmitter Authorization
Wireless signals contain transmitter specific features, which can be used to verify the identity of transmitters and assist in implementing an authentication and authorization system. Most recently, there has been wide i…
Deep LearningOpen Set LearningOpen-set learning with augmented categories by exploiting unlabelled data
Novel categories are commonly defined as those unobserved during training but present during testing. However, partially labelled training datasets can contain unlabelled training samples that belong to novel categories,…
General ClassificationNovelty Detectionobject-detectionObject Detection+2Implicit supervision for fault detection and segmentation of emerging fault types with Deep Variational Autoencoders
Data-driven fault diagnostics of safety-critical systems often faces the challenge of a complete lack of labeled data associated with faulty system conditions (i.e., fault types) at training time. Since an unknown number…
ClusteringFault DetectionOne-Class ClassificationOne-class classifier+2Are Out-of-Distribution Detection Methods Effective on Large-Scale Datasets?
Supervised classification methods often assume the train and test data distributions are the same and that all classes in the test set are present in the training set. However, deployed classifiers often require the abil…
General Classificationimage-classificationImage ClassificationOpen Set Learning+2Face Detection on Surveillance Images
In last few decades, a lot of progress has been made in the field of face detection. Various face detection methods have been proposed by numerous researchers working in this area. The two well-known benchmarking platfor…
BenchmarkingFace DetectionOpen Set LearningOpen Set Medical Diagnosis
Machine-learned diagnosis models have shown promise as medical aides but are trained under a closed-set assumption, i.e. that models will only encounter conditions on which they have been trained. However, it is practica…
DiagnosticMedical DiagnosisOpen Set LearningOpen Set Recognition Through Deep Neural Network Uncertainty: Does Out-of-Distribution Detection Require Generative Classifiers?
We present an analysis of predictive uncertainty based out-of-distribution detection for different approaches to estimate various models' epistemic uncertainty and contrast it with extreme value theory based open set rec…
Open Set LearningOut-of-Distribution DetectionVisual and Semantic Prototypes-Jointly Guided CNN for Generalized Zero-shot Learning
In the process of exploring the world, the curiosity constantly drives humans to cognize new things. Supposing you are a zoologist, for a presented animal image, you can recognize it immediately if you know its class. Ot…
Generalized Zero-Shot LearningOpen Set LearningZero-Shot LearningPrimate Face Identification in the Wild
Ecological imbalance owing to rapid urbanization and deforestation has adversely affected the population of several wild animals. This loss of habitat has skewed the population of several non-human primate species like c…
Face IdentificationManagementOpen Set LearningKnowledge is Never Enough: Towards Web Aided Deep Open World Recognition
While today's robots are able to perform sophisticated tasks, they can only act on objects they have been trained to recognize. This is a severe limitation: any robot will inevitably see new objects in unconstrained sett…
Open Set LearningUnified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition
Modern deep neural networks are well known to be brittle in the face of unknown data instances and recognition of the latter remains a challenge. Although it is inevitable for continual-learning systems to encounter such…
Audio ClassificationBayesian InferenceContinual LearningOpen Set Learning+1P-ODN: Prototype based Open Deep Network for Open Set Recognition
Most of the existing recognition algorithms are proposed for closed set scenarios, where all categories are known beforehand. However, in practice, recognition is essentially an open set problem. There are categories we …
Open Set LearningTripletAlignment Based Mathching Networks for One-Shot Classification and Open-Set Recognition
Deep learning for object classification relies heavily on convolutional models. While effective, CNNs are rarely interpretable after the fact. An attention mechanism can be used to highlight the area of the image that th…
ClassificationGeneral ClassificationOpen Set LearningAn In-Depth Study on Open-Set Camera Model Identification
Camera model identification refers to the problem of linking a picture to the camera model used to shoot it. As this might be an enabling factor in different forensic applications to single out possible suspects (e.g., d…
Open Set Learning