Dataset creation for deep-learning model

An apparatus, comprising:

• at least one processor;
• and
• a computer readable storage medium having computer readable program code embodied therewith and executable by the at least one processor, the computer readable program code comprising:
• computer readable program code configured to receive a training dataset to be utilized for training a deep-learning model;
• computer readable program code configured to identify a plurality of aspects of the training dataset, wherein each of the plurality of aspects corresponds to one of a plurality of categories of operations that can be performed on the training dataset;
• computer readable program code configured to measure, for each of the plurality of aspects, an amount of variance of the aspect within the training dataset;
• computer readable program code configured to create additional data to be incorporated into the training dataset, wherein the additional data comprise data generated for each of the aspects having a variance less than a predetermined amount, wherein the data generated for an aspect results in the corresponding aspect having an amount of variance at least equal to the predetermined amount;
• and
• computer readable program code configured to incorporate the additional data into the training dataset.

A computer program product, comprising:

• a computer readable storage medium having computer readable program code embodied therewith, the computer readable program code being executable by a processor and comprising:
• computer readable program code configured to receive a training dataset to be utilized for training a deep-learning model;
• computer readable program code configured to identify a plurality of aspects of the training dataset, wherein each of the plurality of aspects corresponds to one of a plurality of categories of operations that can be performed on the training dataset;
• computer readable program code configured to measure, for each of the plurality of aspects, an amount of variance of the aspect within the training dataset;
• computer readable program code configured to create additional data to be incorporated into the training dataset, wherein the additional data comprise data generated for each of the aspects having a variance less than a predetermined amount, wherein the data generated for an aspect results in the corresponding aspect having an amount of variance at least equal to the predetermined amount;
• and
• computer readable program code configured to incorporate the additional data into the training dataset.

A method, comprising:

• receiving (i) a dataset used with a machine-learning model and (ii) a purpose of the machine-learning model;
• identifying dimensions of the dataset, wherein each of the dimensions corresponds to a feature of the dataset;
• measuring, utilizing at least one heuristic, variability in each of the dimensions across the dataset;
• identifying, from the dimensions, non-variable dimensions comprising dimensions having variability less than a predetermined amount, wherein the predetermined amount for each of the dimensions is based upon the purpose of the machine-learning model, the purpose requiring less variability for at least a subset of the dimensions than for another subset of the dimensions;
• and
• augmenting the dataset for each non-variable dimension such that the non-variable dimension, after augmentation, has a variability at least equal to the predetermined amount across the dataset.