Patent ID: 11947570
Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A computer-implemented method for augmenting a dataset, used by a data augmentation application, to train a neural network, the computer-implemented method comprising:
obtaining, by one or more processing units, a first feature vector for input data based on a first model, wherein the first model comprises one or more filters capable of detecting specific objects comprising at least one of eyes, ears, feathers, fur, or hair;
clustering, by one or more processing units, the input data to a plurality of clusters;
obtaining, by one or more processing units, for each cluster of the plurality of clusters, a second feature vector based on the first model;
estimating, by one or more processing units, for each cluster of the plurality of clusters, a similarity between the first feature vector and the second feature vector;
determining, by one or more processing units, at least one cluster of the plurality of clusters for which the similarity is lower than a similarity threshold; and
performing, by one or more processing units, data augmentation to the at least one cluster to train a neural network, wherein performing, by a data augmentation application, the data augmentation comprises:
for each of the at least one cluster:
performing, by one or more processing units, a plurality of augmentation methods on the cluster to obtain a plurality of augmented clusters;
for each cluster of the plurality of augmented clusters:
obtaining, by one or more processing units, a third feature vector for the augmented cluster based on the first model;
calculating, by one or more processing units, a loss based on the first feature vector and the third feature vector;
selecting, by one or more processing units, one of the plurality of augmented clusters for which the loss is the lowest, as an optimal augmented cluster; and
combining, by one or more processing units, the input data with the respective optimal augmented clusters to train a second model.