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AssigneeTata Consultancy Services Limited
InventorsRupayan CHAKRABORTY, Sunil Kumar KOPPARAPU
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Statistical pattern recognition relies on substantial amount of annotated samples for better learning and learning is insufficient in low resource scenarios. Creating annotated databases itself is a challenging task, requires lot of effort and cost, which may not always be feasible. Such challenges are addressed by the present disclosure by generating synthetic samples through automatic transformation using Deep Autoencoders (DAE). An autoencoder is trained using all possible combination of pairs between a plurality of classes that could be formed from a limited number of handful samples in a low resource database, and then the DAE is used to generate new samples when one class samples are given as input to the autoencoder. Again, the system of the present disclosure can be configured to generate number of training samples as required. Also, the deep autoencoder can be dynamically configured to meet requirements.
A processor implemented method for automatic generation of synthetic samples, the method comprising:
A system comprising:
A non-transitory computer readable medium having a computer readable program embodied therein, wherein the computer readable program, when executed on a computing device, causes the computing device to:
Dependencies are inferred from the claim text.