Papers Open-World Semi-Supervised Learning
“Open-World Semi-Supervised Learning” 태그가 달린 논문 18편 · 필터 해제
SECOS: Semantic Capture for Rigorous Classification in Open-World Semi-Supervised Learning
In open-world semi-supervised learning (OWSSL), a model learns from labeled data and unlabeled data containing both known and novel classes. In practical OWSSL applications, models are expected to perform rigorous classi…
Open-World Semi-Supervised LearningSemantic correspondenceLearning Textual Prompts for Open-World Semi-Supervised Learning
Traditional semi-supervised learning achieves significant success in closed-world scenarios. To better align with the openness of the real world, researchers propose open-world semi-supervised learning (OWSSL), which…
Image-text matchingOpen-World Semi-Supervised LearningPrompt LearningText MatchingOwMatch: Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning
Semi-supervised learning (SSL) offers a robust framework for harnessing the potential of unannotated data. Traditionally, SSL mandates that all classes possess labeled instances. However, the emergence of open-world SSL …
Open-World Semi-Supervised LearningRethinking Open-World Semi-Supervised Learning: Distribution Mismatch and Inductive Inference
Open-world semi-supervised learning (OWSSL) extends conventional semi-supervised learning to open-world scenarios by taking account of novel categories in unlabeled datasets. Despite the recent advancements in OWSSL, the…
Open-World Semi-Supervised LearningPrompt-Driven Feature Diffusion for Open-World Semi-Supervised Learning
In this paper, we present a novel approach termed Prompt-Driven Feature Diffusion (PDFD) within a semi-supervised learning framework for Open World Semi-Supervised Learning (OW-SSL). At its core, PDFD deploys an efficien…
Open-World Semi-Supervised LearningRepresentation LearningOpen-World Semi-Supervised Learning for Node Classification
Open-world semi-supervised learning (Open-world SSL) for node classification, that classifies unlabeled nodes into seen classes or multiple novel classes, is a practical but under-explored problem in the graph community.…
ClassificationContrastive LearningNode ClassificationOpen-World Semi-Supervised LearningRobust Semi-Supervised Learning for Self-learning Open-World Classes
Existing semi-supervised learning (SSL) methods assume that labeled and unlabeled data share the same class space. However, in real-world applications, unlabeled data always contain classes not present in the labeled set…
Open-World Semi-Supervised LearningSelf-LearningTargeted Representation Alignment for Open-World Semi-Supervised Learning
Open-world Semi-Supervised Learning aims to classify unlabeled samples utilizing information from labeled data while unlabeled samples are not only from the labeled known categories but also from novel categories pre…
Maximum SeparationOpen-World Semi-Supervised LearningA Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning
Open-world semi-supervised learning aims at inferring both known and novel classes in unlabeled data, by harnessing prior knowledge from a labeled set with known classes. Despite its importance, there is a lack of theore…
ClusteringOpen-World Semi-Supervised LearningRepresentation LearningBridging the Gap: Learning Pace Synchronization for Open-World Semi-Supervised Learning
In open-world semi-supervised learning, a machine learning model is tasked with uncovering novel categories from unlabeled data while maintaining performance on seen categories from labeled data. The central challenge is…
Novel Class DiscoveryOpen-World Semi-Supervised LearningPseudo LabelDiscover and Align Taxonomic Context Priors for Open-world Semi-Supervised Learning
Open-world Semi-Supervised Learning (OSSL) is a realistic and challenging task, aiming to classify unlabeled samples from both seen and novel classes using partially labeled samples from the seen classes. Previous works…
Open-World Semi-Supervised LearningRepresentation LearningDiscover and Align Taxonomic Context Priors for Open-world Semi-Supervised Learning
Open-world Semi-Supervised Learning (OSSL) is a realistic and challenging task, aiming to classify unlabeled samples from both seen and novel classes using partially labeled samples from the seen classes. Previous works…
Open-World Semi-Supervised LearningRepresentation LearningTowards Unbiased Training in Federated Open-world Semi-supervised Learning
Federated Semi-supervised Learning (FedSSL) has emerged as a new paradigm for allowing distributed clients to collaboratively train a machine learning model over scarce labeled data and abundant unlabeled data. However, …
Open-World Semi-Supervised LearningTransfer LearningParametric Classification for Generalized Category Discovery: A Baseline Study
Generalized Category Discovery (GCD) aims to discover novel categories in unlabelled datasets using knowledge learned from labelled samples. Previous studies argued that parametric classifiers are prone to overfitting to…
ClassificationNovel Class DiscoveryOpen-World Semi-Supervised LearningRepresentation LearningTowards Realistic Semi-Supervised Learning
Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solv…
Novel Class DiscoveryOpen-World Semi-Supervised LearningPseudo LabelOpenLDN: Learning to Discover Novel Classes for Open-World Semi-Supervised Learning
Semi-supervised learning (SSL) is one of the dominant approaches to address the annotation bottleneck of supervised learning. Recent SSL methods can effectively leverage a large repository of unlabeled data to improve pe…
Open-World Semi-Supervised LearningGeneralized Category Discovery
In this paper, we consider a highly general image recognition setting wherein, given a labelled and unlabelled set of images, the task is to categorize all images in the unlabelled set. Here, the unlabelled images may co…
Fine-Grained Visual RecognitionOpen-World Semi-Supervised LearningRepresentation LearningOpen-World Semi-Supervised Learning
A fundamental limitation of applying semi-supervised learning in real-world settings is the assumption that unlabeled test data contains only classes previously encountered in the labeled training data. However, this ass…
image-classificationImage ClassificationNovel Object DetectionOpen-World Semi-Supervised Learning