Summary of Deep Clustering with Self-supervision Using Pairwise Similarities, by Mohammadreza Sadeghi et al.
Deep Clustering with Self-Supervision using Pairwise Similarities
by Mohammadreza Sadeghi, Narges Armanfard
First submitted to arxiv on: 6 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel deep clustering framework, dubbed DCSS, which incorporates self-supervision using pairwise similarities to improve clustering performance. The proposed method consists of two phases: the first phase uses an autoencoder trained with cluster-specific losses to form hypersphere-like groups of similar data points in the latent space; the second phase employs pairwise similarities to create a K-dimensional space accommodating more complex cluster distributions, providing better clustering results. The effectiveness of DCSS is demonstrated on seven benchmark datasets through rigorous experiments. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine grouping similar things together, like putting all your favorite books in one box and all your least favorite movies in another. This paper proposes a new way to do that, using a combination of machine learning techniques to find the right groups. It’s like finding the perfect boxes for your stuff! The method is tested on several different datasets and shows promising results. |
Keywords
» Artificial intelligence » Autoencoder » Clustering » Latent space » Machine learning