Papers Open Set Learning
“Open Set Learning” 태그가 달린 논문 267편 · 필터 해제
OpenGCD: Assisting Open World Recognition with Generalized Category Discovery
A desirable open world recognition (OWR) system requires performing three tasks: (1) Open set recognition (OSR), i.e., classifying the known (classes seen during training) and rejecting the unknown (unseen$/$novel classe…
Continual LearningDiversityIncremental LearningOpen Set LearningHomOpt: A Homotopy-Based Hyperparameter Optimization Method
Machine learning has achieved remarkable success over the past couple of decades, often attributed to a combination of algorithmic innovations and the availability of high-quality data available at scale. However, a thir…
Bayesian OptimizationHyperparameter OptimizationOpen Set LearningSMAC+1Learning Large Margin Sparse Embeddings for Open Set Medical Diagnosis
Fueled by deep learning, computer-aided diagnosis achieves huge advances. However, out of controlled lab environments, algorithms could face multiple challenges. Open set recognition (OSR), as an important one, states th…
Medical DiagnosisOpen Set LearningDistill-SODA: Distilling Self-Supervised Vision Transformer for Source-Free Open-Set Domain Adaptation in Computational Pathology
Developing computational pathology models is essential for reducing manual tissue typing from whole slide images, transferring knowledge from the source domain to an unlabeled, shifted target domain, and identifying unse…
Data AugmentationDomain AdaptationOpen Set Learningwhole slide imagesLearning Adversarial Semantic Embeddings for Zero-Shot Recognition in Open Worlds
Zero-Shot Learning (ZSL) focuses on classifying samples of unseen classes with only their side semantic information presented during training. It cannot handle real-life, open-world scenarios where there are test samples…
Open Set LearningZero-Shot LearningOpenClinicalAI: An Open and Dynamic Model for Alzheimer's Disease Diagnosis
Although Alzheimer's disease (AD) cannot be reversed or cured, timely diagnosis can significantly reduce the burden of treatment and care. Current research on AD diagnosis models usually regards the diagnosis task as a t…
DiagnosticMeta-LearningOpen Set LearningOpenAPMax: Abnormal Patterns-based Model for Real-World Alzheimer's Disease Diagnosis
Alzheimer's disease (AD) cannot be reversed, but early diagnosis will significantly benefit patients' medical treatment and care. In recent works, AD diagnosis has the primary assumption that all categories are known a p…
Open Set LearningOpenNDD: Open Set Recognition for Neurodevelopmental Disorders Detection
Since the strong comorbid similarity in NDDs, such as attention-deficit hyperactivity disorder, can interfere with the accurate diagnosis of autism spectrum disorder (ASD), identifying unknown classes is extremely crucia…
open-set classificationOpen Set LearningTowards Open Vocabulary Learning: A Survey
In the field of visual scene understanding, deep neural networks have made impressive advancements in various core tasks like segmentation, tracking, and detection. However, most approaches operate on the close-set assum…
Open Set LearningOut-of-Distribution DetectionScene UnderstandingSegmentation+2Open-Set RF Fingerprinting via Improved Prototype Learning
Deep learning has been widely used in radio frequency (RF) fingerprinting. Despite its excellent performance, most existing methods only consider a closed-set assumption, which cannot effectively tackle signals emitted f…
Open Set LearningFew-Shot Open-Set Learning for On-Device Customization of KeyWord Spotting Systems
A personalized KeyWord Spotting (KWS) pipeline typically requires the training of a Deep Learning model on a large set of user-defined speech utterances, preventing fast customization directly applied on-device. To fill …
Few-Shot LearningKeyword SpottingOpen Set LearningTripletUncovering the Hidden Dynamics of Video Self-supervised Learning under Distribution Shifts
Video self-supervised learning (VSSL) has made significant progress in recent years. However, the exact behavior and dynamics of these models under different forms of distribution shift are not yet known. In this paper, …
Open Set LearningRepresentation LearningSelf-Supervised LearningIn or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation
Out-of-distribution (OOD) detection is the problem of identifying inputs which are unrelated to the in-distribution task. The OOD detection performance when the in-distribution (ID) is ImageNet-1K is commonly being teste…
Open Set LearningOut-of-Distribution DetectionOut of Distribution (OOD) DetectionLearning for Transductive Threshold Calibration in Open-World Recognition
In deep metric learning for visual recognition, the calibration of distance thresholds is crucial for achieving desired model performance in the true positive rates (TPR) or true negative rates (TNR). However, calibratin…
Graph Neural NetworkMetric LearningOpen Set Learningtorchosr -- a PyTorch extension package for Open Set Recognition models evaluation in Python
The article presents the torchosr package - a Python package compatible with PyTorch library - offering tools and methods dedicated to Open Set Recognition in Deep Neural Networks. The package offers two state-of-the-art…
Open Set LearningMDENet: Multi-modal Dual-embedding Networks for Malware Open-set Recognition
Malware open-set recognition (MOSR) aims at jointly classifying malware samples from known families and detect the ones from novel unknown families, respectively. Existing works mostly rely on a well-trained classifier c…
Open Set LearningCNS-Net: Conservative Novelty Synthesizing Network for Malware Recognition in an Open-set Scenario
We study the challenging task of malware recognition on both known and novel unknown malware families, called malware open-set recognition (MOSR). Previous works usually assume the malware families are known to the class…
Open Set LearningGlocal Energy-based Learning for Few-Shot Open-Set Recognition
Few-shot open-set recognition (FSOR) is a challenging task of great practical value. It aims to categorize a sample to one of the pre-defined, closed-set classes illustrated by few examples while being able to reject the…
Open Set LearningOpenMix+: Revisiting Data Augmentation for Open Set Recognition
Open set recognition requires models to recognize samples of known classes learned in the training set while reject unknowns not learned. Compared with the structural risk minimization theory for closed-set problems, str…
Data AugmentationOpen Set LearningUncertainty-inspired Open Set Learning for Retinal Anomaly Identification
Failure to recognize samples from the classes unseen during training is a major limitation of artificial intelligence in the real-world implementation for recognition and classification of retinal anomalies. We establish…
Anomaly ClassificationOpen Set Learning