Open-set learning with augmented categories by exploiting unlabelled data
Novel categories are commonly defined as those unobserved during training but present during testing. However, partially labelled training datasets can contain unlabelled training samples that belong to novel categories, meaning these can be present in training and testing. This research is the first to generalise between what we call observed-novel and unobserved-novel categories within a new learning policy called open-set learning with augmented category by exploiting unlabelled data or Open-LACU. After surveying existing learning policies, we introduce Open-LACU as a unified policy of positive and unlabelled learning, semi-supervised learning and open-set recognition. Subsequently, we develop the first Open-LACU model using an algorithmic training process of the relevant research fields. The proposed Open-LACU classifier achieves state-of-the-art and first-of-its-kind results.
Code (0)
등록된 구현이 없습니다.
Tasks
General ClassificationNovelty Detectionobject-detectionObject DetectionOpen Set LearningSemantic SegmentationSimilar Papers 제목 키워드 기반
CiPR: An Efficient Framework with Cross-instance Positive Relations for Generalized Category Discovery
We tackle the issue of generalized category discovery (GCD). GCD considers the open-world problem of automatically clustering a partially labelled dataset, in which the unlabelled data may contain instances from both nov…
ClusteringContrastive LearningRepresentation LearningHydraMix-Net: A Deep Multi-task Semi-supervised Learning Approach for Cell Detection and Classification
Semi-supervised techniques have removed the barriers of large scale labelled set by exploiting unlabelled data to improve the performance of a model. In this paper, we propose a semi-supervised deep multi-task classifica…
Cell DetectionGeneral ClassificationMulti-Task LearningClass-incremental Novel Class Discovery
We study the new task of class-incremental Novel Class Discovery (class-iNCD), which refers to the problem of discovering novel categories in an unlabelled data set by leveraging a pre-trained model that has been trained…
Incremental LearningKnowledge DistillationNovel Class DiscoverySimplifying Open-Set Video Domain Adaptation with Contrastive Learning
In an effort to reduce annotation costs in action recognition, unsupervised video domain adaptation methods have been proposed that aim to adapt a predictive model from a labelled dataset (i.e., source domain) to an unla…
Action RecognitionContrastive LearningDomain Adaptation