Papers Missing Labels
“Missing Labels” 태그가 달린 논문 139편 · 필터 해제
Empowering Bridge Digital Twins by Bridging the Data Gap with a Unified Synthesis Framework
As critical transportation infrastructure, bridges face escalating challenges from aging and deterioration, while traditional manual inspection methods suffer from low efficiency. Although 3D point cloud technology provi…
Missing LabelsSemantic SegmentationWhen and How Unlabeled Data Provably Improve In-Context Learning
Recent research shows that in-context learning (ICL) can be effective even when demonstrations have missing or incorrect labels. To shed light on this capability, we examine a canonical setting where the demonstrations a…
In-Context LearningMissing LabelsL3A: Label-Augmented Analytic Adaptation for Multi-Label Class Incremental Learning
Class-incremental learning (CIL) enables models to learn new classes continually without forgetting previously acquired knowledge. Multi-label CIL (MLCIL) extends CIL to a real-world scenario where each sample may belong…
class-incremental learningClass Incremental LearningExemplar-FreeIncremental Learning+2Cut out and Replay: A Simple yet Versatile Strategy for Multi-Label Online Continual Learning
Multi-Label Online Continual Learning (MOCL) requires models to learn continuously from endless multi-label data streams, facing complex challenges including persistent catastrophic forgetting, potential missing labels, …
Continual LearningMissing LabelsMulti-Label LearningWhen VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification
Image classification benchmark datasets such as CIFAR, MNIST, and ImageNet serve as critical tools for model evaluation. However, despite the cleaning efforts, these datasets still suffer from pervasive noisy labels and …
image-classificationImage ClassificationMissing LabelsRobust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data
Federated learning (FL) presents an effective solution for collaborative model training while maintaining data privacy across decentralized client datasets. However, data quality issues such as noisy labels, missing clas…
Federated LearningMissing LabelsSynthetic Data GenerationConformal Prediction with Corrupted Labels: Uncertain Imputation and Robust Re-weighting
We introduce a framework for robust uncertainty quantification in situations where labeled training data are corrupted, through noisy or missing labels. We build on conformal prediction, a statistical tool for generating…
Conformal PredictionImputationMissing LabelsPrediction+2Model Evaluation in the Dark: Robust Classifier Metrics with Missing Labels
Missing data in supervised learning is well-studied, but the specific issue of missing labels during model evaluation has been overlooked. Ignoring samples with missing values, a common solution, can introduce bias, espe…
ImputationMissing LabelsMissing ValuesDeep Learning Approaches for Medical Imaging Under Varying Degrees of Label Availability: A Comprehensive Survey
Deep learning has achieved significant breakthroughs in medical imaging, but these advancements are often dependent on large, well-annotated datasets. However, obtaining such datasets poses a significant challenge, as it…
image-classificationImage ClassificationMissing LabelsExploiting Label Skewness for Spiking Neural Networks in Federated Learning
The energy efficiency of deep spiking neural networks (SNNs) aligns with the constraints of resource-limited edge devices, positioning SNNs as a promising foundation for intelligent applications leveraging the extensive …
Federated LearningKnowledge DistillationMissing LabelsExtreme Multi-label Completion for Semantic Document Labelling with Taxonomy-Aware Parallel Learning
In Extreme Multi Label Completion (XMLCo), the objective is to predict the missing labels of a collection of documents. Together with XML Classification, XMLCo is arguably one of the most challenging document classificat…
Document ClassificationMissing LabelsMulti-Task LearningDual-Label Learning With Irregularly Present Labels
In multi-task learning, we often encounter the case when the presence of labels across samples exhibits irregular patterns: samples can be fully labeled, partially labeled or unlabeled. Taking drug analysis as an example…
ImputationMissing LabelsMulti-Task LearningRethinking Prompting Strategies for Multi-Label Recognition with Partial Annotations
Vision-language models (VLMs) like CLIP have been adapted for Multi-Label Recognition (MLR) with partial annotations by leveraging prompt-learning, where positive and negative prompts are learned for each class to associ…
Missing LabelsPrompt LearningDeep Self-Cleansing for Medical Image Segmentation with Noisy Labels
Medical image segmentation is crucial in the field of medical imaging, aiding in disease diagnosis and surgical planning. Most established segmentation methods rely on supervised deep learning, in which clean and precise…
Image SegmentationMedical Image SegmentationMissing LabelsSegmentation+1A Simple and Generalist Approach for Panoptic Segmentation
Generalist vision models aim for one and the same architecture for a variety of vision tasks. While such shared architecture may seem attractive, generalist models tend to be outperformed by their bespoken counterparts, …
Missing LabelsPanoptic SegmentationDifferentiable Logic Programming for Distant Supervision
We introduce a new method for integrating neural networks with logic programming in Neural-Symbolic AI (NeSy), aimed at learning with distant supervision, in which direct labels are unavailable. Unlike prior methods, our…
Missing LabelsCLEANANERCorp: Identifying and Correcting Incorrect Labels in the ANERcorp Dataset
Label errors are a common issue in machine learning datasets, particularly for tasks such as Named Entity Recognition. Such label errors might hurt model training, affect evaluation results, and lead to an inaccurate ass…
Missing Labelsnamed-entity-recognitionNamed Entity RecognitionNEROn the Necessity of World Knowledge for Mitigating Missing Labels in Extreme Classification
Extreme Classification (XC) aims to map a query to the most relevant documents from a very large document set. XC algorithms used in real-world applications learn this mapping from datasets curated from implicit feedback…
ImputationMissing LabelsWorld KnowledgeFrom Lazy to Prolific: Tackling Missing Labels in Open Vocabulary Extreme Classification by Positive-Unlabeled Sequence Learning
Open-vocabulary Extreme Multi-label Classification (OXMC) extends traditional XMC by allowing prediction beyond an extremely large, predefined label set (typically $10^3$ to $10^{12}$ labels), addressing the dynamic natu…
Extreme Multi-Label ClassificationKeyphrase GenerationMissing LabelsMulti-Label Classification+2Text-Region Matching for Multi-Label Image Recognition with Missing Labels
Recently, large-scale visual language pre-trained (VLP) models have demonstrated impressive performance across various downstream tasks. Motivated by these advancements, pioneering efforts have emerged in multi-label ima…
Contrastive LearningMissing LabelsMulti-Label Image RecognitionPseudo Label