Clustering

Supporting Clustering with Contrastive Learning

Introduced by Zhang et al. in Supporting Clustering with Contrastive Learning

SCCL, or Supporting Clustering with Contrastive Learning, is a framework to leverage contrastive learning to promote better separation in unsupervised clustering. It combines the top-down clustering with the bottom-up instance-wise contrastive learning to achieve better inter-cluster distance and intra-cluster distance. During training, we jointly optimize a clustering loss over the original data instances and an instance-wise contrastive loss over the associated augmented pairs.

Source: Supporting Clustering with Contrastive Learning

Papers


Paper Code Results Date Stars

Tasks


Task Papers Share
Language Modelling 1 25.00%
Sentiment Analysis 1 25.00%
Short Text Clustering 1 25.00%
Text Clustering 1 25.00%

Components


Component Type
🤖 No Components Found You can add them if they exist; e.g. Mask R-CNN uses RoIAlign

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