Papers Open Set Learning
“Open Set Learning” 태그가 달린 논문 267편 · 필터 해제
OpenSlot: Mixed Open-Set Recognition with Object-Centric Learning
Existing open-set recognition (OSR) studies typically assume that each image contains only one class label, with the unknown test set (negative) having a disjoint label space from the known test set (positive), a scenari…
Computational Efficiencyobject-detectionObject DetectionOpen Set LearningLarge-Scale Evaluation of Open-Set Image Classification Techniques
The goal for classification is to correctly assign labels to unseen samples. However, most methods misclassify samples with unseen labels and assign them to one of the known classes. Open-Set Classification (OSC) algorit…
image-classificationImage Classificationopen-set classificationOpen Set LearningCascading Unknown Detection with Known Classification for Open Set Recognition
Deep learners tend to perform well when trained under the closed set assumption but struggle when deployed under open set conditions. This motivates the field of Open Set Recognition in which we seek to give deep learner…
Open Set LearningInformed Decision-Making through Advancements in Open Set Recognition and Unknown Sample Detection
Machine learning-based techniques open up many opportunities and improvements to derive deeper and more practical insights from data that can help businesses make informed decisions. However, the majority of these techni…
Decision MakingOpen Set LearningRevealing the Two Sides of Data Augmentation: An Asymmetric Distillation-based Win-Win Solution for Open-Set Recognition
In this paper, we reveal the two sides of data augmentation: enhancements in closed-set recognition correlate with a significant decrease in open-set recognition. Through empirical investigation, we find that multi-sampl…
Data AugmentationKnowledge DistillationOpen Set LearningDynamic Against Dynamic: An Open-set Self-learning Framework
In open-set recognition, existing methods generally learn statically fixed decision boundaries using known classes to reject unknown classes. Though they have achieved promising results, such decision boundaries are evid…
Open Set LearningSelf-LearningOpen-Set Video-based Facial Expression Recognition with Human Expression-sensitive Prompting
In Video-based Facial Expression Recognition (V-FER), models are typically trained on closed-set datasets with a fixed number of known classes. However, these V-FER models cannot deal with unknown classes that are preval…
Facial Expression RecognitionMulti-Task LearningOpen Set LearningPrompt Learning+2Know Yourself Better: Diverse Discriminative Feature Learning Improves Open Set Recognition
Open set recognition (OSR) is a critical aspect of machine learning, addressing the challenge of detecting novel classes during inference. Within the realm of deep learning, neural classifiers trained on a closed set of …
DiversityOpen Set LearningOut-of-Distribution Data: An Acquaintance of Adversarial Examples -- A Survey
Deep neural networks (DNNs) deployed in real-world applications can encounter out-of-distribution (OOD) data and adversarial examples. These represent distinct forms of distributional shifts that can significantly impact…
Adversarial RobustnessAnomaly DetectionOpen Set LearningOpen-Set Recognition in the Age of Vision-Language Models
Are vision-language models (VLMs) for open-vocabulary perception inherently open-set models because they are trained on internet-scale datasets? We answer this question with a clear no - VLMs introduce closed-set assumpt…
Open Set LearningROG$_{PL}$: Robust Open-Set Graph Learning via Region-Based Prototype Learning
Open-set graph learning is a practical task that aims to classify the known class nodes and to identify unknown class samples as unknowns. Conventional node classification methods usually perform unsatisfactorily in open…
DenoisingGraph LearningNode ClassificationOpen Set LearningTaking Class Imbalance Into Account in Open Set Recognition Evaluation
In recent years Deep Neural Network-based systems are not only increasing in popularity but also receive growing user trust. However, due to the closed-world assumption of such systems, they cannot recognize samples from…
Open Set LearningAll Beings Are Equal in Open Set Recognition
In open-set recognition (OSR), a promising strategy is exploiting pseudo-unknown data outside given $K$ known classes as an additional $K$+$1$-th class to explicitly model potential open space. However, treating unknown …
AllContrastive LearningOpen Set LearningOpen-Set Facial Expression Recognition
Facial expression recognition (FER) models are typically trained on datasets with a fixed number of seven basic classes. However, recent research works point out that there are far more expressions than the basic ones. T…
Facial Expression RecognitionFacial Expression Recognition (FER)Open Set LearningExploring Diverse Representations for Open Set Recognition
Open set recognition (OSR) requires the model to classify samples that belong to closed sets while rejecting unknown samples during test. Currently, generative models often perform better than discriminative models in OS…
Open Set LearningFrom Coarse to Fine-Grained Open-Set Recognition
Open-set recognition (OSR) methods aim to identify whether or not a test example belongs to a category ob- served during training. Depending on how visually sim- ilar a test example is to the training categories the …
Fine-Grained Visual CategorizationOpen Set LearningA Survey on Open-Set Image Recognition
Open-set image recognition (OSR) aims to both classify known-class samples and identify unknown-class samples in the testing set, which supports robust classifiers in many realistic applications, such as autonomous drivi…
Autonomous DrivingMedical DiagnosisOpen Set LearningSurveyClass Information Guided Reconstruction for Automatic Modulation Open-Set Recognition
Automatic Modulation Recognition (AMR) is a crucial technology in the domains of radar and communications. Traditional AMR approaches assume a closed-set scenario, where unknown samples are forcibly misclassified into kn…
Automatic Modulation RecognitionDenoisingMathematical ProofsOpen Set LearningAdvancing Image Retrieval with Few-Shot Learning and Relevance Feedback
With such a massive growth in the number of images stored, efficient search in a database has become a crucial endeavor managed by image retrieval systems. Image Retrieval with Relevance Feedback (IRRF) involves iterativ…
Binary ClassificationClassificationFew-Shot LearningImage Retrieval+3Managing the unknown: a survey on Open Set Recognition and tangential areas
In real-world scenarios classification models are often required to perform robustly when predicting samples belonging to classes that have not appeared during its training stage. Open Set Recognition addresses this issu…
Continual LearningNovelty DetectionOpen Set LearningOut-of-Distribution Detection