Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
Domain-Incremental Learning for Audio Classification
In this work, we propose a method for domain-incremental learning for audio classification from a sequence of datasets recorded in different acoustic conditions. Fine-tuning a model on a sequence of evolving domains or d…
Audio ClassificationClassificationIncremental LearningMulti-Label Classification+1Batch Selection for Multi-Label Classification Guided by Uncertainty and Dynamic Label Correlations
The accuracy of deep neural networks is significantly influenced by the effectiveness of mini-batch construction during training. In single-label scenarios, such as binary and multi-class classification tasks, it has bee…
Multi-class ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONThe Multiplex Classification Framework: optimizing multi-label classifiers through problem transformation, ontology engineering, and model ensembling
Classification is a fundamental task in machine learning. While conventional methods-such as binary, multiclass, and multi-label classification-are effective for simpler problems, they may not adequately address the comp…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONRA-SGG: Retrieval-Augmented Scene Graph Generation Framework via Multi-Prototype Learning
Scene Graph Generation (SGG) research has suffered from two fundamental challenges: the long-tailed predicate distribution and semantic ambiguity between predicates. These challenges lead to a bias towards head predicate…
Graph GenerationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONRetrieval+1Can Large Language Models Serve as Effective Classifiers for Hierarchical Multi-Label Classification of Scientific Documents at Industrial Scale?
We address the task of hierarchical multi-label classification (HMC) of scientific documents at an industrial scale, where hundreds of thousands of documents must be classified across thousands of dynamic labels. The rap…
ClassificationDocument ClassificationHierarchical Multi-label ClassificationMulti-Label Classification+1Incomplete Multi-view Multi-label Classification via a Dual-level Contrastive Learning Framework
Recently, multi-view and multi-label classification have become significant domains for comprehensive data analysis and exploration. However, incompleteness both in views and labels is still a real-world scenario for mul…
ClassificationContrastive LearningLearning TheoryMulti-Label Classification+1Multi-Label Contrastive Learning : A Comprehensive Study
Multi-label classification, which involves assigning multiple labels to a single input, has emerged as a key area in both research and industry due to its wide-ranging applications. Designing effective loss functions is …
Contrastive LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-label Sequential Sentence Classification via Large Language Model
Sequential sentence classification (SSC) in scientific publications is crucial for supporting downstream tasks such as fine-grained information retrieval and extractive summarization. However, current SSC methods are con…
Contrastive LearningExtractive SummarizationIn-Context LearningInformation Retrieval+7Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts
Large language models (LLMs) play a crucial role in natural language processing (NLP) tasks, improving the understanding, generation, and manipulation of human language across domains such as translating, summarizing, an…
Data AugmentationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONLTCXNet: Advancing Chest X-Ray Analysis with Solutions for Long-Tailed Multi-Label Classification and Fairness Challenges
Chest X-rays (CXRs) often display various diseases with disparate class frequencies, leading to a long-tailed, multi-label data distribution. In response to this challenge, we explore the Pruned MIMIC-CXR-LT dataset, a c…
Data AugmentationDecoderDiagnosticFairness+3Semantic segmentation on multi-resolution optical and microwave data using deep learning
Presently, deep learning and convolutional neural networks (CNNs) are widely used in the fields of image processing, image classification, object identification and many more. In this work, we implemented convolutional n…
Deep Learningimage-classificationImage ClassificationMulti-Label Classification+2Cross- and Intra-image Prototypical Learning for Multi-label Disease Diagnosis and Interpretation
Recent advances in prototypical learning have shown remarkable potential to provide useful decision interpretations associating activation maps and predictions with class-specific training prototypes. Such prototypical l…
Interpretable Machine LearningMulti-Label ClassificationMulti-Label Classification on ChestX-ray14Thorax Disease DetectionClassifier Chain Networks for Multi-Label Classification
The classifier chain is a widely used method for analyzing multi-labeled data sets. In this study, we introduce a generalization of the classifier chain: the classifier chain network. The classifier chain network enables…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONA Similarity-Based Oversampling Method for Multi-label Imbalanced Text Data
In real-world applications, as data availability increases, obtaining labeled data for machine learning (ML) projects remains challenging due to the high costs and intensive efforts required for data annotation. Many ML …
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti Label Text ClassificationMulti-Label Text Classification+2Label Cluster Chains for Multi-Label Classification
Multi-label classification is a type of supervised machine learning that can simultaneously assign multiple labels to an instance. To solve this task, some methods divide the original problem into several sub-problems (l…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONLarge Language Models for Patient Comments Multi-Label Classification
Patient experience and care quality are crucial for a hospital's sustainability and reputation. The analysis of patient feedback offers valuable insight into patient satisfaction and outcomes. However, the unstructured n…
De-identificationFew-Shot LearningIn-Context LearningMulti-Label Classification+7Prototypical Extreme Multi-label Classification with a Dynamic Margin Loss
Extreme Multi-label Classification (XMC) methods predict relevant labels for a given query in an extremely large label space. Recent works in XMC address this problem using deep encoders that project text descriptions to…
Contrastive LearningExtreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Hoeffding adaptive trees for multi-label classification on data streams
Data stream learning is a very relevant paradigm because of the increasing real-world scenarios generating data at high velocities and in unbounded sequences. Stream learning aims at developing models that can process in…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONRE-tune: Incremental Fine Tuning of Biomedical Vision-Language Models for Multi-label Chest X-ray Classification
In this paper we introduce RE-tune, a novel approach for fine-tuning pre-trained Multimodal Biomedical Vision-Language models (VLMs) in Incremental Learning scenarios for multi-label chest disease diagnosis. RE-tune free…
Computational EfficiencyIncremental LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Similarity-Dissimilarity Loss for Multi-label Supervised Contrastive Learning
Supervised contrastive learning has achieved remarkable success by leveraging label information; however, determining positive samples in multi-label scenarios remains a critical challenge. In multi-label supervised cont…
Contrastive LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti Label Text Classification+3