Summary of Differentially Private Representation Learning Via Image Captioning, by Tom Sander et al.
Differentially Private Representation Learning via Image Captioning
by Tom Sander, Yaodong Yu, Maziar Sanjabi, Alain Durmus, Yi Ma, Kamalika Chaudhuri, Chuan Guo
First submitted to arxiv on: 4 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 Differentially private machine learning is crucial for training models from sensitive data while preserving privacy. However, existing approaches often sacrifice accuracy for better privacy. This paper focuses on differentially private representation learning and tackles the sub-optimal trade-off between privacy and accuracy in this domain. The authors propose a novel approach that utilizes image captioning to achieve effective DP representation learning. They demonstrate the effectiveness of their method by training a differentially private image captioner (DP-Cap) from scratch on a large-scale multimodal dataset, LAION-2B. This allows for high-quality image features that can be used in various downstream vision and vision-language tasks. For instance, under a privacy budget of ε=8, the proposed model achieves an accuracy of 65.8% on ImageNet-1K, surpassing the previous state-of-the-art (SOTA) of 56.5%. This work has significant implications for achieving better privacy-accuracy trade-offs in differentially private representation learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to train a model that can learn from sensitive data without revealing personal information. The problem is that most models that do this sacrifice their accuracy, which makes it harder to use them in real-life applications. The authors propose a new way of doing this called differentially private image captioning. They show that using this method, they can train a model that can learn from a huge dataset and produce high-quality features. These features can be used for tasks like recognizing images and understanding text. For example, their model achieved an accuracy of 65.8% on a benchmark test, which is better than previous models. |
Keywords
* Artificial intelligence * Image captioning * Machine learning * Representation learning
