Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
Towards Evaluating the Robustness of Deep Diagnostic Models by Adversarial Attack
Deep learning models (with neural networks) have been widely used in challenging tasks such as computer-aided disease diagnosis based on medical images. Recent studies have shown deep diagnostic models may not be robust …
Adversarial AttackDiagnosticMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONModeling Multi-Label Action Dependencies for Temporal Action Localization
Real-world videos contain many complex actions with inherent relationships between action classes. In this work, we propose an attention-based architecture that models these action relationships for the task of temporal …
Action DetectionAction LocalizationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1RotoGrad: Gradient Homogenization in Multitask Learning
Multitask learning is being increasingly adopted in applications domains like computer vision and reinforcement learning. However, optimally exploiting its advantages remains a major challenge due to the effect of negati…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Task LearningLearning Graph Convolutional Networks for Multi-Label Recognition and Applications
The task of multi-label image recognition is to predict a set of object labels that present in an image. As objects normally co-occur in an image, it is desirable to model label dependencies to improve recognition perfor…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label Image RecognitionMulti-label Image Recognition with Partial LabelsMulti-label Classification via Adaptive Resonance Theory-based Clustering
This paper proposes a multi-label classification algorithm capable of continual learning by applying an Adaptive Resonance Theory (ART)-based clustering algorithm and the Bayesian approach for label probability computati…
ClassificationClusteringContinual LearningGeneral Classification+2COVID-19 Tweets Analysis through Transformer Language Models
Understanding the public sentiment and perception in a healthcare crisis is essential for developing appropriate crisis management techniques. While some studies have used Twitter data for predictive modelling during COV…
ManagementMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSentiment AnalysisEfficient Online ML API Selection for Multi-Label Classification Tasks
Multi-label classification tasks such as OCR and multi-object recognition are a major focus of the growing machine learning as a service industry. While many multi-label prediction APIs are available, it is challenging f…
General Classificationimage-classificationImage ClassificationMulti-Label Classification+8Evaluating Multi-label Classifiers with Noisy Labels
Multi-label classification (MLC) is a generalization of standard classification where multiple labels may be assigned to a given sample. In the real world, it is more common to deal with noisy datasets than clean dataset…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONDeep Learning based Multi-Source Localization with Source Splitting and its Effectiveness in Multi-Talker Speech Recognition
Multi-source localization is an important and challenging technique for multi-talker conversation analysis. This paper proposes a novel supervised learning method using deep neural networks to estimate the direction of a…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Top-$k$ eXtreme Contextual Bandits with Arm Hierarchy
Motivated by modern applications, such as online advertisement and recommender systems, we study the top-$k$ extreme contextual bandits problem, where the total number of arms can be enormous, and the learner is allowed …
Computational EfficiencyExtreme Multi-Label ClassificationMulti-Armed BanditsMulti-Label Classification+2Comprehensive Comparative Study of Multi-Label Classification Methods
Multi-label classification (MLC) has recently received increasing interest from the machine learning community. Several studies provide reviews of methods and datasets for MLC and a few provide empirical comparisons of M…
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONZero Training Overhead Portfolios for Learning to Solve Combinatorial Problems
There has been an increasing interest in harnessing deep learning to tackle combinatorial optimization (CO) problems in recent years. Typical CO deep learning approaches leverage the problem structure in the model archit…
BIG-bench Machine LearningCombinatorial OptimizationDeep LearningModel Selection+2ReliefE: Feature Ranking in High-dimensional Spaces via Manifold Embeddings
Feature ranking has been widely adopted in machine learning applications such as high-throughput biology and social sciences. The approaches of the popular Relief family of algorithms assign importances to features by it…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONVocal Bursts Intensity PredictionA shallow neural model for relation prediction
Knowledge graph completion refers to predicting missing triples. Most approaches achieve this goal by predicting entities, given an entity and a relation. We predict missing triples via the relation prediction. To this e…
Knowledge Graph CompletionmodelMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+3In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning
The recent research in semi-supervised learning (SSL) is mostly dominated by consistency regularization based methods which achieve strong performance. However, they heavily rely on domain-specific data augmentations, wh…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONPseudo LabelSemi-Supervised Image Classification+2Joint Learning of Hyperbolic Label Embeddings for Hierarchical Multi-label Classification
We consider the problem of multi-label classification where the labels lie in a hierarchy. However, unlike most existing works in hierarchical multi-label classification, we do not assume that the label-hierarchy is know…
General ClassificationHierarchical Multi-label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Evaluation of Deep Learning Models for Hostility Detection in Hindi Text
The social media platform is a convenient medium to express personal thoughts and share useful information. It is fast, concise, and has the ability to reach millions. It is an effective place to archive thoughts, share …
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONText DetectionWord EmbeddingsTask Adaptive Pretraining of Transformers for Hostility Detection
Identifying adverse and hostile content on the web and more particularly, on social media, has become a problem of paramount interest in recent years. With their ever increasing popularity, fine-tuning of pretrained Tran…
Binary ClassificationClassificationGeneral ClassificationMulti-Label Classification+1Distribution-Free, Risk-Controlling Prediction Sets
While improving prediction accuracy has been the focus of machine learning in recent years, this alone does not suffice for reliable decision-making. Deploying learning systems in consequential settings also requires cal…
BIG-bench Machine LearningClassificationDecision MakingGeneral Classification+9PDAN: Pyramid Dilated Attention Network for Action Detection
Handling long and complex temporal information is an important challenge for action detection tasks. This challenge is further aggravated by densely distributed actions in untrimmed videos. Previous action detection meth…
Action DetectionAction LocalizationMulti-Label ClassificationTemporal Action Localization