Papers Multi-Label Learning
“Multi-Label Learning” 태그가 달린 논문 334편 · 필터 해제
LD-SDM: Language-Driven Hierarchical Species Distribution Modeling
We focus on the problem of species distribution modeling using global-scale presence-only data. Most previous studies have mapped the range of a given species using geographical and environmental features alone. To captu…
Language ModelingLanguage ModellingLarge Language ModelMulti-Label LearningVision-Language Pseudo-Labels for Single-Positive Multi-Label Learning
This paper presents a novel approach to Single-Positive Multi-label Learning. In general multi-label learning, a model learns to predict multiple labels or categories for a single input image. This is in contrast with st…
image-classificationImage ClassificationLanguage ModelingLanguage Modelling+1Neural Collapse in Multi-label Learning with Pick-all-label Loss
We study deep neural networks for the multi-label classification (MLab) task through the lens of neural collapse (NC). Previous works have been restricted to the multi-class classification setting and discovered a preval…
AllMulti-class ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Multi-Label Feature Selection Using Adaptive and Transformed Relevance
Multi-label learning has emerged as a crucial paradigm in data analysis, addressing scenarios where instances are associated with multiple class labels simultaneously. With the growing prevalence of multi-label data acro…
feature selectionimage-classificationImage ClassificationMulti-Label LearningCan Class-Priors Help Single-Positive Multi-Label Learning?
Single-positive multi-label learning (SPMLL) is a typical weakly supervised multi-label learning problem, where each training example is annotated with only one positive label. Existing SPMLL methods typically assign pse…
Multi-Label LearningMulti-Label Noise Transition Matrix Estimation with Label Correlations: Theory and Algorithm
Noisy multi-label learning has garnered increasing attention due to the challenges posed by collecting large-scale accurate labels, making noisy labels a more practical alternative. Motivated by noisy multi-class learnin…
Multi-Label LearningMulti-Label Knowledge Distillation
Existing knowledge distillation methods typically work by imparting the knowledge of output logits or intermediate feature maps from the teacher network to the student network, which is very successful in multi-class sin…
Binary ClassificationKnowledge DistillationMulti-Label LearningExploiting Multi-Label Correlation in Label Distribution Learning
Label Distribution Learning (LDL) is a novel machine learning paradigm that assigns label distribution to each instance. Many LDL methods proposed to leverage label correlation in the learning process to solve the expone…
Multi-Label LearningWhen Measures are Unreliable: Imperceptible Adversarial Perturbations toward Top-$k$ Multi-Label Learning
With the great success of deep neural networks, adversarial learning has received widespread attention in various studies, ranging from multi-class learning to multi-label learning. However, existing adversarial attacks …
Adversarial AttackMulti-Label LearningSemantic-Aware Dual Contrastive Learning for Multi-label Image Classification
Extracting image semantics effectively and assigning corresponding labels to multiple objects or attributes for natural images is challenging due to the complex scene contents and confusing label dependencies. Recent wor…
Contrastive Learningimage-classificationImage ClassificationMulti-Label Image Classification+2Pseudo Labels for Single Positive Multi-Label Learning
The cost of data annotation is a substantial impediment for multi-label image classification: in every image, every category must be labeled as present or absent. Single positive multi-label (SPML) learning is a cost-eff…
image-classificationImage ClassificationMissing LabelsMulti-Label Image Classification+1Hierarchical Multi-Instance Multi-Label Learning for Detecting Propaganda Techniques
Since the introduction of the SemEval 2020 Task 11 (Martino et al., 2020a), several approaches have been proposed in the literature for classifying propaganda based on the rhetorical techniques used to influence readers.…
Multi-Label LearningA Decentralized Spike-based Learning Framework for Sequential Capture in Discrete Perimeter Defense Problem
This paper proposes a novel Decentralized Spike-based Learning (DSL) framework for the discrete Perimeter Defense Problem (d-PDP). A team of defenders is operating on the perimeter to protect the circular territory from …
Multi-Label LearningUnderstanding Label Bias in Single Positive Multi-Label Learning
Annotating data for multi-label classification is prohibitively expensive because every category of interest must be confirmed to be present or absent. Recent work on single positive multi-label (SPML) learning shows tha…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningReviewing Evolution of Learning Functions and Semantic Information Measures for Understanding Deep Learning
A new trend in deep learning, represented by Mutual Information Neural Estimation (MINE) and Information Noise Contrast Estimation (InfoNCE), is emerging. In this trend, similarity functions and Estimated Mutual Informat…
Deep LearningMulti-Label LearningDeep Partial Multi-Label Learning with Graph Disambiguation
In partial multi-label learning (PML), each data example is equipped with a candidate label set, which consists of multiple ground-truth labels and other false-positive labels. Recently, graph-based methods, which demons…
Multi-Label LearningMinimal Learning Machine for Multi-Label Learning
Distance-based supervised method, the minimal learning machine, constructs a predictive model from data by learning a mapping between input and output distance matrices. In this paper, we propose new methods and evaluate…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningTowards Understanding Generalization of Macro-AUC in Multi-label Learning
Macro-AUC is the arithmetic mean of the class-wise AUCs in multi-label learning and is commonly used in practice. However, its theoretical understanding is far lacking. Toward solving it, we characterize the generalizati…
Generalization BoundsMulti-Label LearningAuxiliary Label Embedding for Multi-label Learning with Missing Labels
Label correlation has been exploited for multi-label learning in different ways. Existing approaches presume that label correlation information is available as a prior, but for multi-label datasets having incomplete labe…
Missing LabelsMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningClass-Distribution-Aware Pseudo Labeling for Semi-Supervised Multi-Label Learning
Pseudo-labeling has emerged as a popular and effective approach for utilizing unlabeled data. However, in the context of semi-supervised multi-label learning (SSMLL), conventional pseudo-labeling methods encounter diffic…
Multi-Label LearningPseudo Label