Papers Open Set Learning
“Open Set Learning” 태그가 달린 논문 267편 · 필터 해제
Learning Bounds for Open-Set Learning
Traditional supervised learning aims to train a classifier in the closed-set world, where training and test samples share the same label space. In this paper, we target a more challenging and realistic setting: open-set …
Learning TheoryOpen Set LearningTransfer LearningNon-Exhaustive Learning Using Gaussian Mixture Generative Adversarial Networks
Supervised learning, while deployed in real-life scenarios, often encounters instances of unknown classes. Conventional algorithms for training a supervised learning model do not provide an option to detect such instance…
Open Set LearningDomain Consensus Clustering for Universal Domain Adaptation
In this paper, we investigate Universal Domain Adaptation (UniDA) problem, which aims to transfer the knowledge from source to target under unaligned label space. The main challenge of UniDA lies in how to separate commo…
ClusteringDomain Adaptationdomain classificationOpen Set Learning+2Self-supervised Detransformation Autoencoder for Representation Learning in Open Set Recognition
The objective of Open set recognition (OSR) is to learn a classifier that can reject the unknown samples while classifying the known classes accurately. In this paper, we propose a self-supervision method, Detransformati…
Open Set LearningRepresentation LearningOpening Deep Neural Networks with Generative Models
Image classification methods are usually trained to perform predictions taking into account a predefined group of known classes. Real-world problems, however, may not allow for a full knowledge of the input and label spa…
image-classificationImage ClassificationObject RecognitionOpen Set LearningOpen-set Recognition based on the Combination of Deep Learning and Ensemble Method for Detecting Unknown Traffic Scenarios
An understanding and classification of driving scenarios are important for testing and development of autonomous driving functionalities. Machine learning models are useful for scenario classification but most of them as…
Autonomous DrivingBIG-bench Machine LearningOpen Set LearningTowards Novel Target Discovery Through Open-Set Domain Adaptation
Open-set domain adaptation (OSDA) considers that the target domain contains samples from novel categories unobserved in external source domain. Unfortunately, existing OSDA methods always ignore the demand for the inform…
AttributeDomain AdaptationOpen Set LearningConditional Variational Capsule Network for Open Set Recognition
In open set recognition, a classifier has to detect unknown classes that are not known at training time. In order to recognize new categories, the classifier has to project the input samples of known classes in very comp…
Open Set LearningOpenGAN: Open-Set Recognition via Open Data Generation
Real-world machine learning systems need to analyze test data that may differ from training data. In K-way classification, this is crisply formulated as open-set recognition, core to which is the ability to discriminate …
Open Set LearningLearning Placeholders for Open-Set Recognition
Traditional classifiers are deployed under closed-set setting, with both training and test classes belong to the same set. However, real-world applications probably face the input of unknown categories, and the model wil…
Open Set LearningTeacher-Explorer-Student Learning: A Novel Learning Method for Open Set Recognition
If an unknown example that is not seen during training appears, most recognition systems usually produce overgeneralized results and determine that the example belongs to one of the known classes. To address this problem…
Open Set LearningCollective Decision of One-vs-Rest Networks for Open Set Recognition
Unknown examples that are unseen during training often appear in real-world machine learning tasks, and an intelligent self-learning system should be able to distinguish between known and unknown examples. Accordingly, o…
Open Set LearningSelf-LearningHierarchical Self Attention Based Autoencoder for Open-Set Human Activity Recognition
Wearable sensor based human activity recognition is a challenging problem due to difficulty in modeling spatial and temporal dependencies of sensor signals. Recognition models in closed-set assumption are forced to yield…
Activity RecognitionDecoderHuman Activity RecognitionOpen Set LearningFew-shot Open-set Recognition by Transformation Consistency
In this paper, we attack a few-shot open-set recognition (FSOSR) problem, which is a combination of few-shot learning (FSL) and open-set recognition (OSR). It aims to quickly adapt a model to a given small set of labeled…
Few-Shot LearningOpen Set LearningAdversarial Reciprocal Points Learning for Open Set Recognition
Open set recognition (OSR), aiming to simultaneously classify the seen classes and identify the unseen classes as 'unknown', is essential for reliable machine learning.The key challenge of OSR is how to reduce the empiri…
General ClassificationOpen Set LearningCounterfactual Zero-Shot and Open-Set Visual Recognition
We present a novel counterfactual framework for both Zero-Shot Learning (ZSL) and Open-Set Recognition (OSR), whose common challenge is generalizing to the unseen-classes by only training on the seen-classes. Our idea st…
AttributeBinary ClassificationcounterfactualOpen Set Learning+1Deep Compact Polyhedral Conic Classifier for Open and Closed Set Recognition
In this paper, we propose a new deep neural network classifier that simultaneously maximizes the inter-class separation and minimizes the intra-class variation by using the polyhedral conic classification function. The p…
Anomaly DetectionGeneral ClassificationOpen Set LearningDense outlier detection and open-set recognition based on training with noisy negative images
Deep convolutional models often produce inadequate predictions for inputs foreign to the training distribution. Consequently, the problem of detecting outlier images has recently been receiving a lot of attention. Unlike…
Open Set LearningOutlier DetectionAn Empirical Exploration of Open-Set Recognition via Lightweight Statistical Pipelines
Machine-learned safety-critical systems need to be self-aware and reliably know their unknowns in the open-world. This is often explored through the lens of anomaly/outlier detection or out-of-distribution modeling. One …
open-set classificationOpen Set LearningOutlier DetectionRetrieval+1Task-Adaptive Negative Envision for Few-Shot Open-Set Recognition
We study the problem of few-shot open-set recognition (FSOR), which learns a recognition system capable of both fast adaptation to new classes with limited labeled examples and rejection of unknown negative samples. Trad…
Few-Shot LearningOpen Set Learning