Papers Multi-Label Learning
“Multi-Label Learning” 태그가 달린 논문 334편 · 필터 해제
Pedestrian Attribute Recognition: A Survey
Recognizing pedestrian attributes is an important task in the computer vision community due to it plays an important role in video surveillance. Many algorithms have been proposed to handle this task. The goal of this pa…
AttributeMulti-Label LearningMulti-Task LearningPedestrian Attribute Recognition+1Multi-Label Adversarial Perturbations
Adversarial examples are delicately perturbed inputs, which aim to mislead machine learning models towards incorrect outputs. While most of the existing work focuses on generating adversarial perturbations in multi-class…
General Classificationimage-classificationImage ClassificationMulti-class Classification+4Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction
A capsule is a group of neurons, whose activity vector represents the instantiation parameters of a specific type of entity. In this paper, we explore the capsule networks used for relation extraction in a multi-instance…
Multi-Label LearningRelationRelation ExtractionGroup Preserving Label Embedding for Multi-Label Classification
Multi-label learning is concerned with the classification of data with multiple class labels. This is in contrast to the traditional classification problem where every data instance has a single label. Due to the exponen…
ClassificationDimensionality Reductionfeature selectionGeneral Classification+3KDGAN: Knowledge Distillation with Generative Adversarial Networks
Knowledge distillation (KD) aims to train a lightweight classifier suitable to provide accurate inference with constrained resources in multi-label learning. Instead of directly consuming feature-label pairs, the classif…
Knowledge DistillationMulti-Label LearningPreference Based Adaptation for Learning Objectives
In many real-world learning tasks, it is hard to directly optimize the true performance measures, meanwhile choosing the right surrogate objectives is also difficult. Under this situation, it is desirable to incorporate …
Multi-Label LearningAPLenty: annotation tool for creating high-quality datasets using active and proactive learning
In this paper, we present APLenty, an annotation tool for creating high-quality sequence labeling datasets using active and proactive learning. A major innovation of our tool is the integration of automatic annotation wi…
Active LearningMulti-Label LearningPart-Of-Speech TaggingFew-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces
Large multi-label datasets contain labels that occur thousands of times (frequent group), those that occur only a few times (few-shot group), and labels that never appear in the training dataset (zero-shot group). Multi-…
General ClassificationMulti-Label ClassificationMulti-Label LearningMulti Label Text Classification+3An Interpretable Neural Network with Topical Information for Relevant Emotion Ranking
Text might express or evoke multiple emotions with varying intensities. As such, it is crucial to predict and rank multiple relevant emotions by their intensities. Moreover, as emotions might be evoked by hidden topics, …
Common Sense ReasoningEmotion ClassificationMulti-Label LearningTopic Models+1Making Classifier Chains Resilient to Class Imbalance
Class imbalance is an intrinsic characteristic of multi-label data. Most of the labels in multi-label data sets are associated with a small number of training examples, much smaller compared to the size of the data set. …
Multi-Label LearningCRAFTML, an Efficient Clustering-based Random Forest for Extreme Multi-label Learning
Extreme Multi-label Learning (XML) considers large sets of items described by a number of labels that can exceed one million. Tree-based methods, which hierarchically partition the problem into small scale sub-probl…
ClusteringMulti-Label LearningLearning a Compressed Sensing Measurement Matrix via Gradient Unrolling
Linear encoding of sparse vectors is widely popular, but is commonly data-independent -- missing any possible extra (but a priori unknown) structure beyond sparsity. In this paper we present a new method to learn linear …
compressed sensingDecoderExtreme Multi-Label ClassificationMulti-Label Classification+3Incremental Sparse Bayesian Ordinal Regression
Ordinal Regression (OR) aims to model the ordering information between different data categories, which is a crucial topic in multi-label learning. An important class of approaches to OR models the problem as a linear co…
Multi-Label LearningregressionRelevant Emotion Ranking from Text Constrained with Emotion Relationships
Text might contain or invoke multiple emotions with varying intensities. As such, emotion detection, to predict multiple emotions associated with a given text, can be cast into a multi-label classification problem. We wo…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningTopic ModelsDistribution-based Label Space Transformation for Multi-label Learning
Multi-label learning problems have manifested themselves in various machine learning applications. The key to successful multi-label learning algorithms lies in the exploration of inter-label correlations, which usually …
Dimensionality ReductionMulti-Label LearningMulti-label Learning Based Deep Transfer Neural Network for Facial Attribute Classification
Deep Neural Network (DNN) has recently achieved outstanding performance in a variety of computer vision tasks, including facial attribute classification. The great success of classifying facial attributes with DNN often …
AttributeClassificationDomain AdaptationFace Detection+5Learning to Separate Object Sounds by Watching Unlabeled Video
Perceiving a scene most fully requires all the senses. Yet modeling how objects look and sound is challenging: most natural scenes and events contain multiple objects, and the audio track mixes all the sound sources toge…
Audio DenoisingAudio Source SeparationDenoisingMulti-Label LearningAttentional Multilabel Learning over Graphs: A Message Passing Approach
We address a largely open problem of multilabel classification over graphs. Unlike traditional vector input, a graph has rich variable-size substructures which are related to the labels in some ways. We believe that unco…
General ClassificationGraph Neural NetworkMulti-Label LearningMulti-label Learning with Missing Labels using Mixed Dependency Graphs
This work focuses on the problem of multi-label learning with missing labels (MLML), which aims to label each test instance with multiple class labels given training instances that have an incomplete/partial set of these…
Image RetrievalMissing LabelsMulti-Label LearningRetrieval+1Tips, guidelines and tools for managing multi-label datasets: the mldr.datasets R package and the Cometa data repository
New proposals in the field of multi-label learning algorithms have been growing in number steadily over the last few years. The experimentation associated with each of them always goes through the same phases: selection …
ManagementMulti-Label Learning