Methods for training and testing perception network by using images obtained from multiple imaging devices having diverse specifications and learning device and testing device using the same

Abstract

A method for training a perception network includes (a) perceiving first image-level data obtained from a first imaging device through the perception network to generate first prediction results, and training the perception network based on the first prediction results, (b) augmenting the first and second image-level data, respectively obtained from the first and a second imaging device, through a transfer network to generate first and second feature-level data, perceiving the first and the second feature-level data through the perception network to generate second prediction results, and training the transfer network based on the second prediction results, and (c) augmenting the first and the second image-level data through the transfer network to generate third feature-level data, perceiving the third feature-level data through the perception network to generate third prediction results, and retraining the perception network based on the third prediction results.