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Extreme Multi-Label Classification

Extreme Multi-Label Classification is a supervised learning problem where an instance may be associated with multiple labels. The two main problems are the unbalanced labels in the dataset and the amount of different labels.

Papers

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TitleStatusHype
Cluster-Guided Label Generation in Extreme Multi-Label ClassificationCode1
Review of Extreme Multilabel Classification—0
CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label ClassificationCode0
Uncertainty in Extreme Multi-label Classification—0
ELIAS: End-to-End Learning to Index and Search in Large Output SpacesCode1
On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification—0
Augmenting Training Data for Massive Semantic Matching Models in Low-Traffic E-commerce Stores—0
Investigating Active Learning Sampling Strategies for Extreme Multi Label Text Classification—0
Open Vocabulary Extreme Classification Using Generative Models—0
Extreme Multi-Label Classification with Label Masking for Product Attribute Value Extraction—0
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