https://github.com/jgraving/selfsne

Self-Supervised Noise Embeddings (Self-SNE)
https://github.com/jgraving/selfsne

clustering contrastive-learning deep-learning dimensionality-reduction embedding-models machine-learning self-supervised-learning

Last synced: 6 months ago
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Self-Supervised Noise Embeddings (Self-SNE)

• Host: GitHub
• URL: https://github.com/jgraving/selfsne
• Owner: jgraving
• License: apache-2.0
• Created: 2020-07-18T05:31:23.000Z (about 5 years ago)
• Default Branch: main
• Last Pushed: 2024-04-03T09:20:19.000Z (over 1 year ago)
• Last Synced: 2024-04-03T10:31:46.030Z (over 1 year ago)
• Topics: clustering, contrastive-learning, deep-learning, dimensionality-reduction, embedding-models, machine-learning, self-supervised-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 2.78 MB
• Stars: 151
• Watchers: 31
• Forks: 13
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

          
Self-Supervised Noise Embeddings (Self-SNE)

============

__This is an alpha release currently undergoing development__. Examples and documentation will be added upon release of the accompanying publication. 

Not all features have been validated and may change without notice. Use at your own risk.

Self-SNE is a probabilistic family of self-supervised deep learning models for compressing high-dimensional data to a low-dimensional embedding. It is a general-purpose modelling framework for multiple types of data including images, sequences, and tabular data. It uses self-supervised objectives to preserve structure in the compressed latent space.

License

------------

Released under a Apache 2.0 License. See [LICENSE](https://github.com/jgraving/selfsne/blob/main/LICENSE) for details.