| CPC G06N 3/096 (2023.01) [G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06V 10/778 (2022.01)] | 13 Claims |
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1. A system for optimizing a deep neural network (DNN) model, comprising:
at least one processor configured to execute computer-readable commands, the processor including,
a learning part for learning a deep neural network (DNN)-based style transfer model by using an image of a specific style to be learned,
wherein the style transfer model is a DNN model having an architecture in which the number of deep layers is reduced through transfer learning using a previously learned result and includes a plurality of layers of a previously trained DNN model that has learned an image of a style similar to the specific style,
wherein the previously trained DNN model is a selected DNN model from a list including a plurality of previously trained DNN models based on style similarities between the style transfer model to be trained and each of the plurality of previously trained DNN models and
wherein the plurality of previously trained DNN models are trained based on a style of painting of an image by using a gram matrix.
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