Feature vector generation using neural networks

A data source verifying apparatus for verifying at least one data source that generates new specimen data intended for use in a machine learning infrastructure, the data source verifying apparatus comprising processing circuitry and a memory containing instructions executable by the processing circuitry whereby the data source verifying apparatus is configured to:

• obtain at least one feature vector representing the new specimen data at the data source verification apparatus;
• execute a correlation check between the at least one feature vector and at least one reference feature vector representing trusted specimen data for determination of a correlation measure;
• and to
• register the at least one data source as trusted data source at a subscription management apparatus when the correlation measure is below a first predetermined threshold and the new specimen data is different from the trusted specimen data.

A feature vector crafting apparatus comprising processing circuitry and a memory containing instructions executable by the processing circuitry whereby the feature vector crafting apparatus is configured to:

• receive an identification of at least one trusted data source at the feature vector crafting apparatus;
• acquire at least one saliency map in relation to new specimen data generated at the at least one trusted data source, wherein the at least one saliency map indicates which part of the new specimen data is to be used for obtaining at least one new feature vector;
• and
• craft the at least one new feature vector from the part of new specimen data identified by the at least one saliency map.

A system for automatic generation of at least one feature vector for use in a machine learning infrastructure, the system comprising:

• a data source verifying apparatus for verifying at least one data source that generates new specimen data, wherein the data source verifying apparatus comprises processing circuitry configured to:
• obtain at least one feature vector representing the new specimen data;
• execute a correlation check between the at least one obtained feature vector and at least one reference feature vector representing trusted specimen data for determination of a correlation measure;
• and
• register the at least one data source as at least one trusted data source at a subscription management apparatus when the correlation measure is below a first predetermined threshold and the new specimen data is different from the trusted specimen data;
• and
• a feature vector crafting apparatus that comprises processing circuitry configured to:
• receive an identification of the at least one trusted data source;
• acquire at least one saliency map in relation to the new specimen data generated at the at least one trusted data source, wherein the at least one saliency map indicates which part of the new specimen data is to be used for obtaining at least one new feature vector;
• and
• craft the at least one new feature vector from the part of new specimen data identified by the at least one saliency map.