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About Clustering With Deep Learning: Taxonomy and New Methods
This document proposes a taxonomy for clustering methods that utilize deep neural networks. The taxonomy aims to provide a systematic way for researchers and practitioners to create new clustering methods by selectively combining aspects of existing approaches. The taxonomy outlines common building blocks of deep learning-based clustering methods, including the neural network architecture, features used for clustering, losses, training procedures, and optional re-running of clustering after training. To demonstrate the taxonomy, the authors conduct a case study applying it to develop a new method using a fully convolutional autoencoder with a two-phase training procedure.
Detailed Information
| Author: | ['rather Aarif'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 12 |
| Language: | English |
| Format: | |
| Price: | FREE |
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