PROCÉDÉ D'APPRENTISSAGE ET DISPOSITIF D'APPRENTISSAGE POUR AJUSTER LES PARAMÈTRES D'UN CNN DANS LEQUEL DES RÉSEAUX RÉSIDUELS SONT FOURNIS POUR LE MÉTA-APPRENTISSAGE ET PROCÉDÉ DE TEST ET DISPOSITIF DE TEST LES UTILISANT

摘要

A CNN-based method for meta learning, i.e., learning to learning, by using a learning device including convolutional layers capable of applying convolution operations to an image or its corresponding input feature maps to generate output feature maps, and residual networks capable of feed-forwarding the image or its corresponding input feature maps to next convolutional layer through bypassing the convolutional layers or its sub-convolutional layers is provided. The CNN-based method includes steps of: the learning device (a) selecting a specific residual network to be dropped out among the residual networks; (b) feeding the image into a transformed CNN where the specific residual network is dropped out, and outputting a CNN output; and (c) calculating losses by using the CNN output and its corresponding GT, and adjusting parameters of the transformed CNN. Further, the CNN-based method can be also applied to layer-wise dropout, stochastic ensemble, virtual driving, and the like.