clabrugere/pytorch-scarf
Implementation of SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption in Pytorch, a model learning a representation of tabular data using contrastive learning. It is inspired from SimCLR and uses a similar architecture and loss.
This is a machine learning tool for data scientists and researchers who work with structured, tabular datasets. It takes your raw tabular data and generates meaningful numerical representations (embeddings) for each row, even without labeled examples. These embeddings can then be used for tasks like clustering, classification, or anomaly detection, helping you find patterns or make predictions more effectively.
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Use this if you need to extract valuable insights or prepare tabular data for machine learning when you don't have enough labeled examples for traditional supervised learning.
Not ideal if you are working with image, text, or time-series data, as this tool is specifically designed for tabular datasets.
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Language
Python
License
MIT
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Last pushed
Mar 17, 2024
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