Papers Multi-Label Learning
“Multi-Label Learning” 태그가 달린 논문 334편 · 필터 해제
ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning
Deep learning-based electrocardiogram (ECG) classification has shown impressive performance but clinical adoption has been slowed by the lack of transparent and faithful explanations. Post hoc methods such as saliency ma…
Contrastive LearningDeep LearningDiagnosticECG Classification+3Uncertainty-Aware Global-View Reconstruction for Multi-View Multi-Label Feature Selection
In recent years, multi-view multi-label learning (MVML) has gained popularity due to its close resemblance to real-world scenarios. However, the challenge of selecting informative features to ensure both performance and …
feature selectionMulti-Label LearningMulti-label feature selection based on binary hashing learning and dynamic graph constraints
Multi-label learning poses significant challenges in extracting reliable supervisory signals from the label space. Existing approaches often employ continuous pseudo-labels to replace binary labels, improving supervisory…
feature selectionMulti-Label LearningPseudo LabelGSBA$^K$: $top$-$K$ Geometric Score-based Black-box Attack
Existing score-based adversarial attacks mainly focus on crafting $top$-1 adversarial examples against classifiers with single-label classification. Their attack success rate and query efficiency are often less than sati…
Multi-Label LearningLabel Ranker: Self-Aware Preference for Classification Label Position in Visual Masked Self-Supervised Pre-Trained Model
This paper investigates the impact of randomly initialized unique encoding of classification label position on the visual masked self-supervised pre-trained model when fine-tuning downstream classification tasks. Our fin…
ClassificationDimensionality ReductionHuman Activity RecognitionImage Classification+3Improving Multi-Label Contrastive Learning by Leveraging Label Distribution
In multi-label learning, leveraging contrastive learning to learn better representations faces a key challenge: selecting positive and negative samples and effectively utilizing label information. Previous studies select…
Contrastive LearningMulti-Label LearningA Simple but Effective Closed-form Solution for Extreme Multi-label Learning
Extreme multi-label learning (XML) is a task of assigning multiple labels from an extremely large set of labels to each data instance. Many current high-performance XML models are composed of a lot of hyperparameters, wh…
FormMulti-Label LearningIncomplete Multi-View Multi-label Learning via Disentangled Representation and Label Semantic Embedding
In incomplete multi-view multi-label learning scenarios, it is crucial to use the incomplete multi-view data to extract consistent and specific representations from different data sources and to fully exploit the mis…
Multi-Label LearningUnsupervised Pre-trainingTheory-Inspired Deep Multi-View Multi-Label Learning with Incomplete Views and Noisy Labels
Incomplete features and label noise in multi-view multi-label data significantly undermine the reliability and performance, motivating researchers to explore the mechanism of representation and information recovery. …
Multi-Label LearningContext-Based Semantic-Aware Alignment for Semi-Supervised Multi-Label Learning
Due to the lack of extensive precisely-annotated multi-label data in real word, semi-supervised multi-label learning (SSMLL) has gradually gained attention. Abundant knowledge embedded in vision-language models (VLMs) pr…
Multi-Label LearningTowards Macro-AUC oriented Imbalanced Multi-Label Continual Learning
In Continual Learning (CL), while existing work primarily focuses on the multi-class classification task, there has been limited research on Multi-Label Learning (MLL). In practice, MLL datasets are often class-imbalance…
Continual LearningMulti-class ClassificationMulti-Label LearningEDGE: Unknown-aware Multi-label Learning by Energy Distribution Gap Expansion
Multi-label Out-Of-Distribution (OOD) detection aims to discriminate the OOD samples from the multi-label In-Distribution (ID) ones. Compared with its multiclass counterpart, it is crucial to model the joint information …
Multi-Label LearningOut of Distribution (OOD) DetectionProbably Approximately Precision and Recall Learning
Precision and Recall are foundational metrics in machine learning where both accurate predictions and comprehensive coverage are essential, such as in recommender systems and multi-label learning. In these tasks, balanci…
Multi-Label LearningPAC learningRecommendation SystemsImplicit Regularization for Multi-label Feature Selection
In this paper, we address the problem of feature selection in the context of multi-label learning, by using a new estimator based on implicit regularization and label embedding. Unlike the sparse feature selection method…
feature selectionMulti-Label LearningTask-Augmented Cross-View Imputation Network for Partial Multi-View Incomplete Multi-Label Classification
In real-world scenarios, multi-view multi-label learning often encounters the challenge of incomplete training data due to limitations in data collection and unreliable annotation processes. The absence of multi-view fea…
ImputationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningEvolving Text Data Stream Mining
A text stream is an ordered sequence of text documents generated over time. A massive amount of such text data is generated by online social platforms every day. Designing an algorithm for such text streams to extract us…
Multi-Label LearningDual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-Supervised Multi-Label Learning
Semi-supervised multi-label learning (SSMLL) is a powerful framework for leveraging unlabeled data to reduce the expensive cost of collecting precise multi-label annotations. Unlike semi-supervised learning, one cannot s…
Multi-Label LearningPseudo LabelMulti-Label Learning with Stronger Consistency Guarantees
We present a detailed study of surrogate losses and algorithms for multi-label learning, supported by $H$-consistency bounds. We first show that, for the simplest form of multi-label loss (the popular Hamming loss), the …
Multi-Label LearningTowards Enhanced Classification of Abnormal Lung sound in Multi-breath: A Light Weight Multi-label and Multi-head Attention Classification Method
This study aims to develop an auxiliary diagnostic system for classifying abnormal lung respiratory sounds, enhancing the accuracy of automatic abnormal breath sound classification through an innovative multi-label learn…
DiagnosticDiversityMulti-Label LearningSound ClassificationMulti-label Learning with Random Circular Vectors
The extreme multi-label classification~(XMC) task involves learning a classifier that can predict from a large label set the most relevant subset of labels for a data instance. While deep neural networks~(DNNs) have demo…
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label Learning