Papers Open Set Learning
“Open Set Learning” 태그가 달린 논문 267편 · 필터 해제
Dense open-set recognition with synthetic outliers generated by Real NVP
Today's deep models are often unable to detect inputs which do not belong to the training distribution. This gives rise to confident incorrect predictions which could lead to devastating consequences in many important ap…
Autonomous Drivingimage-classificationImage ClassificationOpen Set Learning+3Learning Open Set Network with Discriminative Reciprocal Points
Open set recognition is an emerging research area that aims to simultaneously classify samples from predefined classes and identify the rest as 'unknown'. In this process, one of the key challenges is to reduce the risk …
Open Set LearningEmpowering Knowledge Distillation via Open Set Recognition for Robust 3D Point Cloud Classification
Real-world scenarios pose several challenges to deep learning based computer vision techniques despite their tremendous success in research. Deeper models provide better performance, but are challenging to deploy and kno…
3D Point Cloud ClassificationGeneral ClassificationKnowledge DistillationObject Recognition+2Driver Anomaly Detection: A Dataset and Contrastive Learning Approach
Distracted drivers are more likely to fail to anticipate hazards, which result in car accidents. Therefore, detecting anomalies in drivers' actions (i.e., any action deviating from normal driving) contains the utmost imp…
Anomaly DetectionContrastive LearningOpen Set LearningA Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning
Current deep learning methods are regarded as favorable if they empirically perform well on dedicated test sets. This mentality is seamlessly reflected in the resurfacing area of continual learning, where consecutively a…
Active LearningContinual LearningOpen Set LearningOpen-set Adversarial Defense
Open-set recognition and adversarial defense study two key aspects of deep learning that are vital for real-world deployment. The objective of open-set recognition is to identify samples from open-set classes during test…
Adversarial DefenseDecoderDenoisingOpen Set LearningOpen Set Recognition with Conditional Probabilistic Generative Models
Deep neural networks have made breakthroughs in a wide range of visual understanding tasks. A typical challenge that hinders their real-world applications is that unknown samples may be fed into the system during the tes…
Open Set LearningGDumb: A Simple Approach that Questions Our Progress in Continual Learning
We discuss a general formulation for the Continual Learning (CL) problem for classification---a learning task where a stream provides samples to a learner and the goal of the learner, depending on the samples it receives…
class-incremental learningClass Incremental LearningContinual LearningOpen Set LearningRepresentative-Discriminative Learning for Open-set Land Cover Classification of Satellite Imagery
Land cover classification of satellite imagery is an important step toward analyzing the Earth's surface. Existing models assume a closed-set setting where both the training and testing classes belong to the same label s…
ClassificationGeneral ClassificationLand Cover Classificationopen-set classification+1ID-Conditioned Auto-Encoder for Unsupervised Anomaly Detection
In this paper, we introduce ID-Conditioned Auto-Encoder for unsupervised anomaly detection. Our method is an adaptation of the Class-Conditioned Auto-Encoder (C2AE) designed for the open-set recognition. Assuming that no…
Anomaly DetectionOpen Set LearningTask 2Unsupervised Anomaly DetectionDeep Active Learning via Open Set Recognition
In many applications, data is easy to acquire but expensive and time-consuming to label prominent examples include medical imaging and NLP. This disparity has only grown in recent years as our ability to collect data imp…
Active LearningInformativenessOpen Set LearningMMF: A loss extension for feature learning in open set recognition
Open set recognition (OSR) is the problem of classifying the known classes, meanwhile identifying the unknown classes when the collected samples cannot exhaust all the classes. There are many applications for the OSR pro…
Open Set LearningFully Convolutional Open Set Segmentation
In semantic segmentation knowing about all existing classes is essential to yield effective results with the majority of existing approaches. However, these methods trained in a Closed Set of classes fail when new classe…
Open Set LearningSegmentationSemantic SegmentationMore Information Supervised Probabilistic Deep Face Embedding Learning
Researches using margin based comparison loss demonstrate the effectiveness of penalizing the distance between face feature and their corresponding class centers. Despite their popularity and excellent performance, they …
Face RecognitionOpen Set LearningOpen-Set Recognition with Gaussian Mixture Variational Autoencoders
In inference, open-set classification is to either classify a sample into a known class from training or reject it as an unknown class. Existing deep open-set classifiers train explicit closed-set classifiers, in some ca…
ClassificationClusteringGeneral Classificationopen-set classification+1Generative-Discriminative Feature Representations for Open-Set Recognition
We address the problem of open-set recognition, where the goal is to determine if a given sample belongs to one of the classes used for training a model (known classes). The main challenge in open-set recognition is to d…
Open Set LearningFew-Shot Open-Set Recognition using Meta-Learning
The problem of open-set recognition is considered. While previous approaches only consider this problem in the context of large-scale classifier training, we seek a unified solution for this and the low-shot classificati…
ClassificationGeneral ClassificationMeta-LearningMetric Learning+1Open Set Wireless Transmitter Authorization: Deep Learning Approaches and Dataset Considerations
Due to imperfections in transmitters' hardware, wireless signals can be used to verify their identity in an authorization system. While deep learning was proposed for transmitter identification, the majority of the work …
Anomaly DetectionOpen Set LearningOutlier DetectionBoosting Deep Open World Recognition by Clustering
While convolutional neural networks have brought significant advances in robot vision, their ability is often limited to closed world scenarios, where the number of semantic concepts to be recognized is determined by the…
ClusteringIncremental LearningOpen Set LearningOne-vs-Rest Network-based Deep Probability Model for Open Set Recognition
Unknown examples that are unseen during training often appear in real-world computer vision tasks, and an intelligent self-learning system should be able to differentiate between known and unknown examples. Open set reco…
open-set classificationOpen Set LearningSelf-Learning