Dynamically Selecting Neural Networks for Detecting Predetermined Features

One or more computer-readable non-transitory storage media embodying software that is operable when executed to:

• receive an input for a machine-learning model configured to detect a plurality of predetermined features, the machine-learning model comprising at least a first neural network configured to detect a first subset of the plurality of predetermined features and a second neural network configured to detect a second subset of the plurality of predetermined features;
• generate a detection result by processing the input using the first neural network;
• determine, based on the detection result and one or more detection criteria, that the input includes a feature in the first subset of the plurality of predetermined features;
• and
• in response to the determination, output the detection result as an output of the machine-learning model without using the second neural network to process the input.

A system comprising:

• one or more processors;
• and a non-transitory memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to:
• receive an input for a machine-learning model configured to detect a plurality of predetermined features, the machine-learning model comprising at least a first neural network configured to detect a first subset of the plurality of predetermined features and a second neural network configured to detect a second subset of the plurality of predetermined features;
• generate a detection result by processing the input using the first neural network;
• determine, based on the detection result and one or more detection criteria, that the input includes a feature in the first subset of the plurality of predetermined features;
• and
• in response to the determination, output the detection result as an output of the machine-learning model without using the second neural network to process the input.