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.
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
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
Bonsai -- Diverse and Shallow Trees for Extreme Multi-label Classification
In this paper, we develop a suite of algorithms, called Bonsai, which generalizes the notion of label representation in XMC, and partitions the labels in the representation space to learn shallow trees.
EXTREME MULTI-LABEL CLASSIFICATION MULTI-LABEL CLASSIFICATION MULTI-LABEL LEARNING
Food Ingredients Recognition through Multi-label Learning
• MarcBS/food_ingredients_recognition
Automatically constructing a food diary that tracks the ingredients consumed can help people follow a healthy diet.
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.
Learning Vector-valued Functions with Local Rademacher Complexity
• superlj666/Learning-Vector-valued-Functions-with-Local-Rademacher-Complexity
We consider a general family of problems of which the output space admits vector-valued structure, covering a broad family of important domains, e. g. multi-label learning and multi-class classification.
Variational Autoencoders for Sparse and Overdispersed Discrete Data
Many applications, such as text modelling, high-throughput sequencing, and recommender systems, require analysing sparse, high-dimensional, and overdispersed discrete (count-valued or binary) data.
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.