Data diversity visualization and quantification for machine learning models
US12321866B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 28, 2021 |
| Grant date | Jun 3, 2025 |
| Priority date | — |
| Expiry date | Apr 4, 2044 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Systems and techniques that facilitate data diversity visualization and/or quantification for machine learning models are provided. In various embodiments, a processor can access a first dataset and a second dataset, where a machine learning (ML) model is trained on the first dataset. In various instances, the processor can obtain a first set of latent activations generated by the ML model based on the first dataset, and a second set of latent activations generated by the ML model based on the second dataset. In various aspects, the processor can generate a first set of compressed data points based on the first set of latent activations, and a second set of compressed data points based on the second set of latent activations, via dimensionality reduction. In various instances, a diversity component can compute a diversity score based on the first set of compressed data points and second set of compressed data points.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.