Differential Privacy Technique Proposed to Combat Overfitting in Deep Neural Networks
A research paper explores applying differential privacy to enhance generalization in Deep Neural Networks (DNNs). These systems, while achieving top performance on image, speech, and text datasets, risk learning noise from training data, a vulnerability known as overfitting. This problem negatively impacts performance when models must work with unseen data. In practical scenarios, analysts often have limited datasets for building robust models. The proposed approach aims to mitigate overfitting by leveraging differential privacy mechanisms. The work was published on arXiv, a platform for sharing scientific research. The paper's abstract highlights the double-edged nature of DNNs' powerful learning capabilities. No specific authors, institutions, or dates beyond the publication platform are provided in the given source material.
Key facts
- The research explores using differential privacy to improve generalization in Deep Neural Networks.
- Deep Neural Networks achieve state-of-the-art performance on image, speech, and text datasets.
- These systems are vulnerable to learning noise in training data, known as overfitting.
- Overfitting negatively impacts performance on unseen data.
- Analysts in practical settings typically have limited data for model building.
- The paper was published on the arXiv platform.
- arXivLabs is a framework for community collaborators to develop new features.
- arXiv is committed to values of openness, community, excellence, and user data privacy.
Entities
Institutions
- arXiv