CPC G06V 20/52 (2022.01) [G06F 18/2155 (2023.01); G06F 18/2415 (2023.01); G06F 18/2431 (2023.01); G06N 3/08 (2013.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 20/70 (2022.01)] | 20 Claims |
1. A method of training a neural network model for use in an analysis of a captured image, comprising:
providing the neural network model with an unlabeled first image and a labeled second image to obtain a first reconstructed image and a second reconstructed image specific to an image environment;
providing the neural network model with the unlabeled first image, the labeled second image, an image label of the labeled second image, and a domain label indicating to which category of a first category and a second category an image belongs to obtain a second prediction label for the labeled second image and a domain prediction label for the domain label;
calculating at least one loss function using the unlabeled first image, the labeled second image, the second prediction label for the labeled second image, and the domain prediction label for the domain label; and
updating parameters of the neural network model such that the at least one loss function is minimized.
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