Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
Integration of Autoencoder and Functional Link Artificial Neural Network for Multi-label Classification
Multi-label (ML) classification is an actively researched topic currently, which deals with convoluted and overlapping boundaries that arise due to several labels being active for a particular data instance. We propose a…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONUnderstanding Gender and Racial Disparities in Image Recognition Models
Large scale image classification models trained on top of popular datasets such as Imagenet have shown to have a distributional skew which leads to disparities in prediction accuracies across different subsections of pop…
Fairnessimage-classificationImage ClassificationMulti-Label Classification+1Using Language Models on Low-end Hardware
This paper evaluates the viability of using fixed language models for training text classification networks on low-end hardware. We combine language models with a CNN architecture and put together a comprehensive benchma…
ClassificationLanguage ModelingLanguage ModellingMulti-Label Classification+3Multi-Scale Label Relation Learning for Multi-Label Classification Using 1-Dimensional Convolutional Neural Networks
We present Multi-Scale Label Dependence Relation Networks (MSDN), a novel approach to multi-label classification (MLC) using 1-dimensional convolution kernels to learn label dependencies at multi-scale. Modern multi-labe…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONRelationFine-Grained AutoAugmentation for Multi-Label Classification
Data augmentation is a commonly used approach to improving the generalization of deep learning models. Recent works show that learned data augmentation policies can achieve better generalization than hand-crafted ones. H…
ClassificationData AugmentationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Parameter Selection: Why We Should Pay More Attention to It
The importance of parameter selection in supervised learning is well known. However, due to the many parameter combinations, an incomplete or an insufficient procedure is often applied. This situation may cause misleadin…
Medical Code PredictionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONAttack Transferability Characterization for Adversarially Robust Multi-label Classification
Despite of the pervasive existence of multi-label evasion attack, it is an open yet essential problem to characterize the origin of the adversarial vulnerability of a multi-label learning system and assess its attackabil…
Adversarial AttackClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Explaining the Performance of Multi-label Classification Methods with Data Set Properties
Meta learning generalizes the empirical experience with different learning tasks and holds promise for providing important empirical insight into the behaviour of machine learning algorithms. In this paper, we present a …
Meta-LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONLabel Disentanglement in Partition-based Extreme Multilabel Classification
Partition-based methods are increasingly-used in extreme multi-label classification (XMC) problems due to their scalability to large output spaces (e.g., millions or more). However, existing methods partition the large l…
ClassificationDisentanglementExtreme Multi-Label ClassificationMulti-Label Classification+1Modeling Diagnostic Label Correlation for Automatic ICD Coding
Given the clinical notes written in electronic health records (EHRs), it is challenging to predict the diagnostic codes which is formulated as a multi-label classification task. The large set of labels, the hierarchical …
DiagnosticMedical Code PredictionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Extreme Multi-label Learning for Semantic Matching in Product Search
We consider the problem of semantic matching in product search: given a customer query, retrieve all semantically related products from a huge catalog of size 100 million, or more. Because of large catalog spaces and rea…
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningMulti-layered Semantic Representation Network for Multi-label Image Classification
Multi-label image classification (MLIC) is a fundamental and practical task, which aims to assign multiple possible labels to an image. In recent years, many deep convolutional neural network (CNN) based approaches have …
Classificationimage-classificationImage ClassificationMulti-Label Classification+1Gradient-based Label Binning in Multi-label Classification
In multi-label classification, where a single example may be associated with several class labels at the same time, the ability to model dependencies between labels is considered crucial to effectively optimize non-decom…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONAmalgamating Knowledge From Heterogeneous Graph Neural Networks
In this paper, we study a novel knowledge transfer task in the domain of graph neural networks (GNNs). We strive to train a multi-talented student GNN, without accessing human annotations, that "amalgamates" knowledg…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONTransfer LearningMulti-Label Learning from Single Positive Labels
Predicting all applicable labels for a given image is known as multi-label classification. Compared to the standard multi-class case (where each image has only one label), it is considerably more challenging to annotate …
image-classificationImage ClassificationMissing LabelsMulti-Label Classification+3Learning to Predict Visual Attributes in the Wild
Visual attributes constitute a large portion of information contained in a scene. Objects can be described using a wide variety of attributes which portray their visual appearance (color, texture), geometry (shape, size,…
AttributeContrastive LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Unsupervised Person Re-identification via Multi-Label Prediction and Classification based on Graph-Structural Insight
This paper addresses unsupervised person re-identification (Re-ID) using multi-label prediction and classification based on graph-structural insight. Our method extracts features from person images and produces a graph t…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONPerson Re-Identification+1Residual Networks based Distortion Classification and Ranking for Laparoscopic Image Quality Assessment
Laparoscopic images and videos are often affected by different types of distortion like noise, smoke, blur and nonuniform illumination. Automatic detection of these distortions, followed generally by application of appro…
Image Quality AssessmentMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONA Pseudo Label-wise Attention Network for Automatic ICD Coding
Automatic International Classification of Diseases (ICD) coding is defined as a kind of text multi-label classification problem, which is difficult because the number of labels is very large and the distribution of label…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONPseudo LabelMlTr: Multi-label Classification with Transformer
The task of multi-label image classification is to recognize all the object labels presented in an image. Though advancing for years, small objects, similar objects and objects with high conditional probability are still…
Classificationimage-classificationImage ClassificationMulti-Label Classification+2