Multi-Label Learning
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Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution
Crowd sourcing has become a widely adopted scheme to collect ground truth labels.
DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification
In this work, we present DiSMEC, which is a large-scale distributed framework for learning one-versus-rest linear classifiers coupled with explicit capacity control to control model size.
EXTREME MULTI-LABEL CLASSIFICATION MULTI-LABEL CLASSIFICATION MULTI-LABEL LEARNING
Pedestrian Attribute Recognition: A Survey
• wangxiao5791509/Pedestrian-Attribute-Recognition-Paper-List
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We also review some popular network architectures which have widely applied in the deep learning community.
MULTI-LABEL LEARNING MULTI-TASK LEARNING PEDESTRIAN ATTRIBUTE RECOGNITION
Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction
EMNLP 2018 • WHUNLPLab/Papers-to-read
A capsule is a group of neurons, whose activity vector represents the instantiation parameters of a specific type of entity.
Bonsai - Diverse and Shallow Trees for Extreme Multi-label Classification
Extreme multi-label classification refers to supervised multi-label learning involving hundreds of thousand or even millions of labels.
EXTREME MULTI-LABEL CLASSIFICATION MULTI-LABEL CLASSIFICATION MULTI-LABEL LEARNING
Incremental 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.