Papers Open Set Learning
“Open Set Learning” 태그가 달린 논문 267편 · 필터 해제
Know Your Space: Inlier and Outlier Construction for Calibrating Medical OOD Detectors
We focus on the problem of producing well-calibrated out-of-distribution (OOD) detectors, in order to enable safe deployment of medical image classifiers. Motivated by the difficulty of curating suitable calibration data…
Data AugmentationOpen Set LearningOut-of-Distribution DetectionOut of Distribution (OOD) Detection+1DenseHybrid: Hybrid Anomaly Detection for Dense Open-set Recognition
Anomaly detection can be conceived either through generative modelling of regular training data or by discriminating with respect to negative training data. These two approaches exhibit different failure modes. Consequen…
Anomaly DetectionOpen Set LearningScene SegmentationSemantic SegmentationClass-Specific Semantic Reconstruction for Open Set Recognition
Open set recognition enables deep neural networks (DNNs) to identify samples of unknown classes, while maintaining high classification accuracy on samples of known classes. Existing methods basing on auto-encoder (AE) an…
Open Set LearningDummy Prototypical Networks for Few-Shot Open-Set Keyword Spotting
Keyword spotting is the task of detecting a keyword in streaming audio. Conventional keyword spotting targets predefined keywords classification, but there is growing attention in few-shot (query-by-example) keyword spot…
Keyword SpottingMetric LearningOpen Set LearningTest Time Transform Prediction for Open Set Histopathological Image Recognition
Tissue typology annotation in Whole Slide histological images is a complex and tedious, yet necessary task for the development of computational pathology models. We propose to address this problem by applying Open Set Re…
Data AugmentationOpen Set LearningModel-Agnostic Few-Shot Open-Set Recognition
We tackle the Few-Shot Open-Set Recognition (FSOSR) problem, i.e. classifying instances among a set of classes for which we only have few labeled samples, while simultaneously detecting instances that do not belong to an…
Few-Shot LearningmodelOpen Set LearningMaximum Class Separation as Inductive Bias in One Matrix
Maximizing the separation between classes constitutes a well-known inductive bias in machine learning and a pillar of many traditional algorithms. By default, deep networks are not equipped with this inductive bias and t…
Inductive BiasLong-tail LearningMaximum SeparationOpen Set Learning+1Open-Set Recognition with Gradient-Based Representations
Neural networks for image classification tasks assume that any given image during inference belongs to one of the training classes. This closed-set assumption is challenged in real-world applications where models may enc…
image-classificationImage Classificationopen-set classificationOpen Set LearningOOD Augmentation May Be at Odds with Open-Set Recognition
Despite advances in image classification methods, detecting the samples not belonging to the training classes is still a challenging problem. There has been a burst of interest in this subject recently, which is called O…
Diversityimage-classificationImage ClassificationOpen Set LearningOneRing: A Simple Method for Source-free Open-partial Domain Adaptation
In this paper, we investigate Source-free Open-partial Domain Adaptation (SF-OPDA), which addresses the situation where there exist both domain and category shifts between source and target domains. Under the SF-OPDA set…
Domain AdaptationDomain GeneralizationOpen Set LearningPartial Domain Adaptation+1GlanceNets: Interpretabile, Leak-proof Concept-based Models
There is growing interest in concept-based models (CBMs) that combine high-performance and interpretability by acquiring and reasoning with a vocabulary of high-level concepts. A key requirement is that the concepts be i…
Open Set LearningRepresentation LearningExploring the Open World Using Incremental Extreme Value Machines
Dynamic environments require adaptive applications. One particular machine learning problem in dynamic environments is open world recognition. It characterizes a continuously changing domain where only some classes are s…
Class Incremental LearningComputational EfficiencyFace Recognitionimage-classification+3Evaluating Uncertainty Calibration for Open-Set Recognition
Despite achieving enormous success in predictive accuracy for visual classification problems, deep neural networks (DNNs) suffer from providing overconfident probabilities on out-of-distribution (OOD) data. Yet, accurate…
Open Set LearningBreaking with Fixed Set Pathology Recognition through Report-Guided Contrastive Training
When reading images, radiologists generate text reports describing the findings therein. Current state-of-the-art computer-aided diagnosis tools utilize a fixed set of predefined categories automatically extracted from t…
Open Set LearningThoracic Disease ClassificationRepresentation learning with function call graph transformations for malware open set recognition
Open set recognition (OSR) problem has been a challenge in many machine learning (ML) applications, such as security. As new/unknown malware families occur regularly, it is difficult to exhaust samples that cover all the…
Malware ClassificationOpen Set LearningRepresentation LearningPrototype-based Domain Generalization Framework for Subject-Independent Brain-Computer Interfaces
Brain-computer interface (BCI) is challenging to use in practice due to the inter/intra-subject variability of electroencephalography (EEG). The BCI system, in general, necessitates a calibration technique to obtain subj…
Brain Computer InterfaceDomain GeneralizationEEGElectroencephalogram (EEG)+1Open-set Recognition via Augmentation-based Similarity Learning
The primary assumption of conventional supervised learning or classification is that the test samples are drawn from the same distribution as the training samples, which is called closed set learning or classification. I…
Open Set LearningOpen Set Recognition using Vision Transformer with an Additional Detection Head
Deep neural networks have demonstrated prominent capacities for image classification tasks in a closed set setting, where the test data come from the same distribution as the training data. However, in a more realistic o…
image-classificationImage ClassificationOpen Set LearningPMAL: Open Set Recognition via Robust Prototype Mining
Open Set Recognition (OSR) has been an emerging topic. Besides recognizing predefined classes, the system needs to reject the unknowns. Prototype learning is a potential manner to handle the problem, as its ability to im…
DiversityOpen Set LearningopenFEAT: Improving Speaker Identification by Open-set Few-shot Embedding Adaptation with Transformer
Household speaker identification with few enrollment utterances is an important yet challenging problem, especially when household members share similar voice characteristics and room acoustics. A common embedding space …
Open Set LearningSpeaker Identification