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Multi-Label Learning

9 papers with code ยท Methodology

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Adversarial Partial Multi-Label Learning

15 Sep 2019

Partial multi-label learning (PML), which tackles the problem of learning multi-label prediction models from instances with overcomplete noisy annotations, has recently started gaining attention from the research community.

MULTI-LABEL LEARNING

Student Performance Prediction with Optimum Multilabel Ensemble Model

6 Sep 2019

One of the important measures of quality of education is the performance of students in the academic settings.

MULTI-LABEL CLASSIFICATION MULTI-LABEL LEARNING

Intra-Camera Supervised Person Re-Identification: A New Benchmark

27 Aug 2019

Existing person re-identification (re-id) methods rely mostly on a large set of inter-camera identity labelled training data, requiring a tedious data collection and annotation process therefore leading to poor scalability in practical re-id applications.

MULTI-LABEL LEARNING PERSON RE-IDENTIFICATION

Bayesian Network Based Label Correlation Analysis For Multi-label Classifier Chain

6 Aug 2019

Classifier chain (CC) is a multi-label learning approach that constructs a sequence of binary classifiers according to a label order.

MULTI-LABEL LEARNING

Deep Ranking Based Cost-sensitive Multi-label Learning for Distant Supervision Relation Extraction

25 Jul 2019

Furthermore, to deal with the problem of class imbalance in distant supervision relation extraction, we further adopt cost-sensitive learning to rescale the costs from the positive and negative labels.

INFORMATION RETRIEVAL MULTI-LABEL LEARNING RELATION EXTRACTION

Semi-Supervised Graph Embedding for Multi-Label Graph Node Classification

12 Jul 2019

The dimension of the label vector is the same as that of the node vector before the last convolution operation of GCN.

GRAPH CLASSIFICATION GRAPH EMBEDDING MULTI-LABEL CLASSIFICATION MULTI-LABEL LEARNING NODE CLASSIFICATION

Towards Interpretable Deep Extreme Multi-label Learning

3 Jul 2019

In this paper, we discuss the machine learning interpretability of a real-world application, eXtreme Multi-label Learning (XML), which involves learning models from annotated data with many pre-defined labels.

MULTI-LABEL LEARNING

Weakly Supervised Person Re-Identification

CVPR 2019

In the conventional person re-id setting, it is assumed that the labeled images are the person images within the bounding box for each individual; this labeling across multiple nonoverlapping camera views from raw video surveillance is costly and time-consuming.

MULTI-LABEL LEARNING PERSON RE-IDENTIFICATION

Atlas of Digital Pathology: A Generalized Hierarchical Histological Tissue Type-Annotated Database for Deep Learning

CVPR 2019

Quantitative results support the visually consistency of our data and we demonstrate a tissue type-based visual attention aid as a sample tool that could be developed from our database.

MULTI-LABEL LEARNING

A Submodular Feature-Aware Framework for Label Subset Selection in Extreme Classification Problems

NAACL 2019

We can then solve efficiently the problem of multi-label learning with an intractably large number of interdependent labels, such as automatic tagging of Wikipedia pages.

MULTI-LABEL LEARNING