Papers Open Set Learning
“Open Set Learning” 태그가 달린 논문 267편 · 필터 해제
Large-Scale Long-Tailed Recognition in an Open World
Real world data often have a long-tailed and open-ended distribution. A practical recognition system must classify among majority and minority classes, generalize from a few known instances, and acknowledge novelty upon …
ClassificationFew-Shot LearningGeneral Classificationimbalanced classification+3C2AE: Class Conditioned Auto-Encoder for Open-set Recognition
Models trained for classification often assume that all testing classes are known while training. As a result, when presented with an unknown class during testing, such closed-set assumption forces the model to classify …
ClassificationDecoderGeneral Classificationimage-classification+2Open-Set Recognition Using Intra-Class Splitting
This paper proposes a method to use deep neural networks as end-to-end open-set classifiers. It is based on intra-class data splitting. In open-set recognition, only samples from a limited number of known classes are ava…
General ClassificationOpen Set LearningAlignment Based Matching Networks for One-Shot Classification and Open-Set Recognition
Deep learning for object classification relies heavily on convolutional models. While effective, CNNs are rarely interpretable after the fact. An attention mechanism can be used to highlight the area of the image that th…
ClassificationGeneral ClassificationOpen Set LearningDeep CNN-based Multi-task Learning for Open-Set Recognition
We propose a novel deep convolutional neural network (CNN) based multi-task learning approach for open-set visual recognition. We combine a classifier network and a decoder network with a shared feature extractor network…
DecoderGeneral Classificationimage-classificationImage Classification+2The Importance of Metric Learning for Robotic Vision: Open Set Recognition and Active Learning
State-of-the-art deep neural network recognition systems are designed for a static and closed world. It is usually assumed that the distribution at test time will be the same as the distribution during training. As a res…
Active LearningMetric LearningOpen Set LearningIndividual common dolphin identification via metric embedding learning
Photo-identification (photo-id) of dolphin individuals is a commonly used technique in ecological sciences to monitor state and health of individuals, as well as to study the social structure and distribution of a popula…
Open Set LearningTripletClassification-Reconstruction Learning for Open-Set Recognition
Open-set classification is a problem of handling `unknown' classes that are not contained in the training dataset, whereas traditional classifiers assume that only known classes appear in the test environment. Existing o…
ClassificationGeneral Classificationopen-set classificationOpen Set LearningRecent Advances in Open Set Recognition: A Survey
In real-world recognition/classification tasks, limited by various objective factors, it is usually difficult to collect training samples to exhaust all classes when training a recognizer or classifier. A more realistic …
General ClassificationOpen Set LearningSurveyTextbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension
In this work, we introduce a novel algorithm for solving the textbook question answering (TQA) task which describes more realistic QA problems compared to other recent tasks. We mainly focus on two related issues with an…
Open Set LearningQuestion AnsweringReading ComprehensionSelf-Supervised LearningLearning the Compositional Spaces for Generalized Zero-shot Learning
This paper studies the problem of Generalized Zero-shot Learning (G-ZSL), whose goal is to classify instances belonging to both seen and unseen classes at the test time. We propose a novel space decomposition method to s…
Generalized Zero-Shot LearningOpen Set LearningZero-Shot LearningDistribution Networks for Open Set Learning
In open set learning, a model must be able to generalize to novel classes when it encounters a sample that does not belong to any of the classes it has seen before. Open set learning poses a realistic learning scenario t…
General ClassificationOpen Set LearningQuery Attack via Opposite-Direction Feature:Towards Robust Image Retrieval
Most existing works of adversarial samples focus on attacking image recognition models, while little attention is paid to the image retrieval task. In this paper, we identify two inherent challenges in applying prevailin…
Adversarial AttackGeneral ClassificationImage RetrievalOpen Set Learning+1Open Set Learning with Counterfactual Images
In open set recognition, a classifier must label instances of known classes while detecting instances of unknown classes not encountered during training. To detect unknown classes while still generalizing to new instance…
ClassificationcounterfactualGeneral Classificationimage-classification+3Collective decision for open set recognition
In open set recognition (OSR), almost all existing methods are designed specially for recognizing individual instances, even these instances are collectively coming in batch. Recognizers in decision either reject or cate…
Open Set LearningAP18-OLR Challenge: Three Tasks and Their Baselines
The third oriental language recognition (OLR) challenge AP18-OLR is introduced in this paper, including the data profile, the tasks and the evaluation principles. Following the events in the last two years, namely AP16-O…
Open Set LearningLearning a Neural-network-based Representation for Open Set Recognition
Open set recognition problems exist in many domains. For example in security, new malware classes emerge regularly; therefore malware classification systems need to identify instances from unknown classes in addition to …
General ClassificationMalware ClassificationOpen Set LearningRecent Advances in Zero-shot Recognition
With the recent renaissance of deep convolution neural networks, encouraging breakthroughs have been achieved on the supervised recognition tasks, where each class has sufficient training data and fully annotated trainin…
Open Set LearningZero-Shot LearningDenoising Autoencoders for Overgeneralization in Neural Networks
Despite the recent developments that allowed neural networks to achieve impressive performance on a variety of applications, these models are intrinsically affected by the problem of overgeneralization, due to their part…
DenoisingOpen Set LearningAdversarial Robustness: Softmax versus Openmax
Deep neural networks (DNNs) provide state-of-the-art results on various tasks and are widely used in real world applications. However, it was discovered that machine learning models, including the best performing DNNs, s…
Adversarial RobustnessOpen Set Learning