CPC G06V 10/764 (2022.01) [G06F 18/2321 (2023.01); G06F 18/285 (2023.01); G06N 3/045 (2023.01); G06V 20/47 (2022.01); G06V 40/113 (2022.01); G06V 40/28 (2022.01)] | 20 Claims |
1. A method for assigning a classification to an input signal comprising:
computing, using a neural network, a first confidence score for a first input signal, the first confidence score corresponding to a first class represented in the first input signal;
computing, using a confusion factor between the first class and a second class, a first normalization amount corresponding to the first class, the confusion factor representative of a probability that the neural network will compute, for a second input signal associated with the second class, an output indicating that the second input signal corresponds to the first class;
generating a first normalized confidence score corresponding to the first class by adjusting the first confidence score according to the first normalization amount calculated using the confusion factor;
applying a filter to the first normalized confidence score to generate a final confidence score corresponding to the first class; and
determining a final classification for the first input signal using the final confidence score corresponding to the first class.
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