US 2025/0378377 A1 · DETECTING MODEL MEMORIZATION WITH LOCAL INTRINSIC DIMENSIONALITY

Abstract

Local intrinsic dimensionality (LID), when evaluated on a data sample for a generative model, can be used to detect model memorization by comparing the LID determined according to the model parameters with a threshold. This allows detection of memorization by the generative model that reproduces a training data sample as well as memorization that presents low degrees of freedom relative to a ground truth dimensionality of the data set. When data samples are generated by the generative model, the LID of the data samples is evaluated to detect memorization, and memorized data samples may be prevented from delivery as generated data samples. During training, training data samples are evaluated for memorization and may be used to modify the training process to reduce memorization.

Claims20

Dependent claims

A system, comprising:

one or more processors that execute instructions;
and
one or more computer-readable media having instructions executable by the one or more processors for:
identifying a data sample generated responsive to a query by a trained generative model having a set of trained parameters;
estimating a local intrinsic dimensionality of the data sample according to the set of trained parameters of the trained generative model;
determining that the data sample is memorized by the trained generative model when the local intrinsic dimensionality of the data sample is below a threshold;
and
responsive to determining that the data sample is memorized, preventing delivery of the data sample as a response to the query.

5 more elements

Dependencies are inferred from the claim text.

Detecting model memorization with local intrinsic dimensionality

Life of this patent

Detecting model memorization with local intrinsic dimensionality

Life of this patent

Abstract

Claims20