Summary of Privacy-preserving Datasets by Capturing Feature Distributions with Conditional Vaes, By Francesco Di Salvo and David Tafler and Sebastian Doerrich and Christian Ledig
Privacy-preserving datasets by capturing feature distributions with Conditional VAEs
by Francesco Di Salvo, David Tafler, Sebastian Doerrich, Christian Ledig
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers address the challenge of advancing deep learning applications in domains where large and well-annotated datasets are scarce or impossible to obtain by a single entity. They propose a novel approach using Conditional Variational Autoencoders (CVAEs) trained on feature vectors extracted from pre-trained vision foundation models. The CVAE is able to capture the embedding space of a given data distribution, generating synthetic feature vectors that are diverse, privacy-respecting, and potentially unbounded. The method outperforms traditional approaches in both medical and natural image domains, exhibiting higher robustness against perturbations while preserving sample privacy. This work has significant implications for deep learning applications in data-scarce and privacy-sensitive environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this research, scientists are trying to solve a big problem. They want to make it easier to train computers to do new tasks by sharing data with other people. But sometimes, they worry that sharing the data might put some private information at risk. To fix this issue, they created a new way of using special computer models called Conditional Variational Autoencoders (CVAEs). These models are trained on lots of existing data and can generate new, fake data that is very similar to real data. This helps keep the private information safe while still allowing scientists to train their computers. |
Keywords
» Artificial intelligence » Deep learning » Embedding space
