System and methods for automatically augmenting machine learning training dataset by using deep generative models
AssigneesConstructor Autonomous AG, Constructor Education and Research Genossenschaft
InventorsAndrei Boiarov, Igor Bykovskih, Nikita Koritsky, Ilya Shimchik, Serg Bell, Stanislav Protasov, Nikolay Dobrovolskiy, Sergey Ulasen
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Abstract
Systems and methods for augmenting a training dataset. The method includes gaining access to at least one insufficient training dataset for training a neural network NN. A generative convolutional neural network GCNN is trained using the training set or a subset thereof. At least one additional item is generated by the GCNN trained on the existing training set, and the generated item is added to the original training set.
Claims19
A method for automatically augmenting a machine training dataset, the method comprising:
- gaining access to a training dataset for training a neural network NN;
- using at least one sufficiency criteria, determining that the training dataset is insufficient for training the neural network NN;
- selecting a generative convolutional neural network GCNN for which the existing training dataset is sufficient to get trained;
- training the generative convolutional neural network GCNN using the training dataset or a subset of the training dataset;
- generating, by the GCNN trained on the existing training dataset, an additional item;
- and
- adding the additional item to the training dataset.
A system for automatically augmenting an insufficient training dataset for a neural network NN, the system comprising:
- a data storage configured to store a training dataset;
- a data generator comprising a generative convolutional neural network GCNN configured to be trained on a same type of datasets as the NN and to output, after training, a new generated item;
- and
- a dataset augmenter configured to add the new generated item to the training dataset.
Dependencies are inferred from the claim text.