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Unveiling Privacy, Memorization, and Input Curvature Links
March 1, 2024, 5:42 a.m. | Deepak Ravikumar, Efstathia Soufleri, Abolfazl Hashemi, Kaushik Roy
cs.LG updates on arXiv.org arxiv.org
Abstract: Deep Neural Nets (DNNs) have become a pervasive tool for solving many emerging problems. However, they tend to overfit to and memorize the training set. Memorization is of keen interest since it is closely related to several concepts such as generalization, noisy learning, and privacy. To study memorization, Feldman (2019) proposed a formal score, however its computational requirements limit its practical use. Recent research has shown empirical evidence linking input loss curvature (measured by the …
abstract arxiv become concepts cs.ai cs.cr cs.lg neural nets privacy set study tool training type
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