beta
/Cross-model Format Comparison
Abstract

A machine learning model representation is obtained from a model source and information characterizing the layers of the model representation is extracted to result in extracted model information. This extracted model information can be compared to information characterizing one or more known (i.e., previously characterized) machine learning models in order to determine whether there is a match based on layer information. A match can, in some cases, be used to determine an identity of the underlying machine learning model for the model representation. Information regarding the comparison (i.e., the model matching determination) can be provided to a consuming application or process. Related apparatus, systems, techniques and articles are also described.

Full Text

What is claimed is:

A machine learning model representation is obtained from a model source and information characterizing the layers of the model representation is extracted to result in extracted model information. This extracted model information can be compared to information characterizing one or more known (i.e., previously characterized) machine learning models in order to determine whether there is a match based on layer information. A match can, in some cases, be used to determine an identity of the underlying machine learning model for the model representation. Information regarding the comparison (i.e., the model matching determination) can be provided to a consuming application or process. Related apparatus, systems, techniques and articles are also described.
Timeline
Filed
03/05/2026
Published
07/09/2026
Granted
Not Available
IPC Codes(1)
G06N 20/20:Ensemble learning