Systems for multi-task joint training of neural networks using multi-label datasets

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United States of America

PATENT NO 12243292
APP PUB NO 20240078792A1
SERIAL NO

17929449

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Abstract

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Systems and methods for multi-task joint training of a neural network including an encoder module and a multi-headed attention mechanism are provided. In one aspect, the system includes a processor configured to receive input data including a first set of labels and a second set of labels. Using the encoder module, features are extracted from the input data. Using a multi-headed attention mechanism, training loss metrics are computed. A first training loss metric is computed using the extracted features and the first set of labels, and a second training loss metric is computed using the extracted features and the second set of labels. A first mask is applied to filter the first training loss metric, and a second mask is applied to filter the second training loss metric. A final training loss metric is computed based on the filtered first and second training loss metrics.

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Patent Owner(s)

  • LEMON INC.

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Inventor(s)

Inventor Name Address # of filed Patents Total Citations
Cheng, Shuo Los Angeles, US 18 71
Luo, Linjie Los Angeles, US 72 1307
Ma, Wanchun Los Angeles, US 6 5

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