Unseen Class Discovery in Open-world Classification
This paper concerns open-world classification, where the classifier not only needs to classify test examples into seen classes that have appeared in training but also reject examples from unseen or novel classes that have not appeared in training. Specifically, this paper focuses on discovering the hidden unseen classes of the rejected examples. Clearly, without prior knowledge this is difficult. However, we do have the data from the seen training classes, which can tell us what kind of similarity/difference is expected for examples from the same class or from different classes. It is reasonable to assume that this knowledge can be transferred to the rejected examples and used to discover the hidden unseen classes in them. This paper aims to solve this problem. It first proposes a joint open classification model with a sub-model for classifying whether a pair of examples belongs to the same or different classes. This sub-model can serve as a distance function for clustering to discover the hidden classes of the rejected examples. Experimental results show that the proposed model is highly promising.
Code (1)
Tasks
ClassificationClusteringGeneral ClassificationSimilar Papers 제목 키워드 기반
Cross-domain Open-world Discovery
In many real-world applications, test data may commonly exhibit categorical shifts, characterized by the emergence of novel classes, as well as distribution shifts arising from feature distributions different from the on…
image-classificationImage ClassificationNovel Class Discovery for Open Set Raga Classification
The task of Raga classification in Indian Art Music (IAM) is constrained by the limited availability of labeled datasets, resulting in many Ragas being unrepresented during the training of machine learning models. Tradit…
ClassificationContrastive LearningInformation RetrievalMusic Information Retrieval+1OpenGCD: Assisting Open World Recognition with Generalized Category Discovery
A desirable open world recognition (OWR) system requires performing three tasks: (1) Open set recognition (OSR), i.e., classifying the known (classes seen during training) and rejecting the unknown (unseen$/$novel classe…
Continual LearningDiversityIncremental LearningOpen Set LearningHyperbolic Multimodal Representation Learning for Biological Taxonomies
Taxonomic classification in biodiversity research involves organizing biological specimens into structured hierarchies based on evidence, which can come from multiple modalities such as images and genetic information. We…
Representation LearningCategory Discovery: An Open-World Perspective
Category discovery (CD) is an emerging open-world learning task, which aims at automatically categorizing unlabelled data containing instances from unseen classes, given some labelled data from seen classes. This task ha…
Representation Learning