Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
DeMix: Debugging Training Data with Mixed Data Error Types by Investigating Influence Vectors
High-quality training data is essential for the success of machine learning models. However, real-world datasets often contain mixed types of errors arising from systematic flaws in data preparation pipelines, including …
Multi-Label ClassificationRecommendation SystemsREMEDI: A Benchmark for Retention and Unlearning Evaluation in Multi-label Clinical Disease Inference
Language models trained for clinical disease inference are trained on patient data, which may include sensitive and private information, and data owners may request the removal of their data from a trained model due to p…
Multi-Label ClassificationMulti-class ClassificationAddressing Imbalance in Multi-Label Data via Label-Specific Distance-based Oversampling
The complex imbalanced label distribution poses a crucial challenge to multi-label classification, as most classifiers are biased towards the majority class and high-frequent labels. Oversampling is an efficient and flex…
Multi-Label ClassificationHASTE: Hardware-Aware Dynamic Sparse Training for Large Output Spaces
Extreme multi-label classification (XMC) involves learning models over large output spaces with millions of labels, making the output layer a memory-compute bottleneck. While sparsity-based methods reduce arithmetic comp…
Multi-Label ClassificationTADDLE: A Tool-Augmented Agent for Detecting Deficient LLM-Generated Peer Reviews
LLM-generated peer reviews are increasingly common at major venues, yet their deficiencies are hard to detect because they are uniformly fluent and well-structured. Existing work either classifies authorship without judg…
Multi-Label ClassificationBenchmarking Convolutional, Transformer, Hybrid, and Vision Language Models for Multi Disease Retinal Screening
Modern deep learning offers powerful tools for automated retinal screening, but it remains unclear how different visual model families compare in realistic multi-disease settings and under domain shift. In this work, we …
Multi-Label ClassificationWhen Does Synthetic Patent Data Help? Volume-Fidelity Trade-offs in Low-Resource Multi-Label Classification
The issues that must be considered regarding the utilization of synthetic data generated through LLMs for multilabel patent classification include (i) when the use of such data may help and (ii) why. Indeed, the former p…
Multi-Label ClassificationPromptRad: Knowledge-Enhanced Multi-Label Prompt-Tuning for Low-Resource Radiology Report Labeling
Automatic report labeling facilitates the identification of clinical findings from unstructured text and enables large-scale annotation for medical imaging research. Existing rule-based labelers struggle with the diverse…
Multi-Label ClassificationHexagonalWarriorMamba: Superior Threshold-Dependent Multi-label Classification of 12-Lead ECG Cardiac Abnormalities
The accurate automated diagnosis of cardiac abnormalities from 12-lead electrocardiograms (ECGs) is critical for managing cardiovascular disease. However, detecting concurrent conditions remains a challenge for tradition…
Multi-Label ClassificationECG ClassificationRAPT: Retrieval-Augmented Post-hoc Thresholding for Multi-Label Classification
Industrial multi-label document understanding pipelines score candidate labels and threshold or rank them to form a label set per document. This early selection step directly affects the accuracy of downstream informatio…
Multi-Label ClassificationInformation ExtractionMetric LearningELDOR: A Dataset and Benchmark for Illegal Gold Mining in the Amazon Rainforest
Illegal gold mining in the Amazon rainforest causes deforestation, water contamination, and long-term ecosystem disruption, yet remains difficult to monitor at fine spatial scales. Satellite imagery supports large-scale …
Multi-Label ClassificationSemantic SegmentationHybrid Quantum-MambaVision: A Quantum-enhanced State Space Model for Calibrated Mixed-type Wafer Defect Detection
Extracting actionable knowledge from industrial visual data is fundamentally bottlenecked by extreme class imbalance and the prohibitive computational complexity of modern foundation models. In semi-conductor manufacturi…
Multi-Label ClassificationRepresentation LearningAnomaly DetectionD2ACE: Multi-Label Batch Selection Guided by Dual Dynamics and Adaptive Correlation Enhancement
Batch selection is crucial for improving both training efficiency and predictive performance in deep multi-label classification (MLC). Existing batch selection methods typically rely on a single metric to assess instance…
Multi-Label ClassificationShifaMind: A Multiplicative Concept Bottleneck for Interpretable ICD-10 Coding
Automated ICD-10 coding from clinical discharge summaries requires models that are both accurate on long-tailed multi-label classification tasks and interpretable to clinicians. Concept Bottleneck Models (CBMs) offer a p…
Multi-Label ClassificationImproving Imbalanced Multi-Label Chest X-Ray Diagnosis via CBAM-Enhanced CNN Backbones
Chest radiography is a widely used imaging modality for thoracic disease diagnosis, yet its conventional interpretation remains time-consuming and heavily dependent on expert knowledge. While deep learning has improved d…
Multi-Label ClassificationHow Label Imbalance Shapes Geometry: A General Spectral Analysis of Multi-Label Neural Collapse
This work investigates the phenomenon of Neural Collapse (NC) in multi-label classification, extending its conceptual framework from multi-class learning to general correlated and imbalanced multi-label settings. Althoug…
Multi-Label ClassificationMulti-Label LearningDepression Risk Assessment in Social Media via Large Language Models
Depression is one of the most prevalent and debilitating mental health conditions worldwide, frequently underdiagnosed and undertreated. The proliferation of social media platforms provides a rich source of naturalistic …
Multi-Label ClassificationMetaDent: Labeling Clinical Images for Vision-Language Models in Dentistry
Vision-Language Models (VLMs) have demonstrated significant potential in medical image analysis, yet their application in intraoral photography remains largely underexplored due to the lack of fine-grained, annotated dat…
Multi-Label ClassificationVisual Question AnsweringImage CaptioningCXR-LT 2026 Challenge: Multi-Center Long-Tailed and Zero Shot Chest X-ray Classification
Chest X-ray (CXR) interpretation is hindered by the long-tailed distribution of pathologies and the open-world nature of clinical environments. Existing benchmarks often rely on closed-set classes from a single instituti…
Multi-Label ClassificationIncomplete Multi-View Multi-Label Classification via Shared Codebook and Fused-Teacher Self-Distillation
Although multi-view multi-label learning has been extensively studied, research on the dual-missing scenario, where both views and labels are incomplete, remains largely unexplored. Existing methods mainly rely on contra…
Multi-Label ClassificationRepresentation LearningMulti-Label LearningContrastive Learning