Open-World Semi-Supervised Learning
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Benchmarks
Most implemented
OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning
Open-World Semi-Supervised Learning for Node Classification
Robust Semi-Supervised Learning for Self-learning Open-World Classes
Targeted Representation Alignment for Open-World Semi-Supervised Learning
Papers
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 Learning