Electronic device for training neural network model performing image enhancement and controlling method thereof

A method of controlling an electronic device for training a neural network model performing image enhancement, the method comprising:

• obtaining a plurality of first loss values by inputting a first training image from among a first plurality of training images into a plurality of neural network models;
• identifying a loss value having a smallest size from among the plurality of first loss values;
• identifying the first training image as being included in a first training image group for a first neural network model, corresponding to the loss value identified from among the plurality of first loss values, from among the plurality of neural network models;
• obtaining a plurality of loss values by inputting a second training image from among the first plurality of training images into the plurality of neural network models;
• identifying a second loss value having a smallest size from among the plurality of second loss values;
• identifying the second training image as being included in a second training image group for a second neural network model, corresponding to the loss value identified from among the plurality of second loss values, from among the plurality of neural network models;
• training the first neural network model by inputting a second plurality of training images included in the first training image group into the first neural network model;
• and
• training the second neural network model by inputting a third plurality of training images included in the second training image group into the second neural network model.

A non-transitory computer-readable recording medium having instructions recorded thereon, that, when executed by one or more processors, causes the one or more processors to:

• obtain a plurality of first loss values by inputting a first training image from among a first plurality of training images into a plurality of neural network models;
• identify a loss value having a smallest size from among the plurality of first loss values;
• identify the first training image as being included in a first training image group for a first neural network model, corresponding to the loss value identified from among the plurality of first loss values, from among the plurality of neural network models;
• obtain a plurality of second loss values by inputting a second training image from among the first plurality of training images into the plurality of neural network models;
• identify a loss value having a smallest size from among the plurality of second loss values;
• identify the second training image as being included in a second training image group for a second neural network model, corresponding to the loss value identified from among the plurality of second loss values, from among the plurality of neural network models;
• and
• train the first neural network model by inputting a second plurality of training images included in the first training image group into the first neural network model, and train the second neural network model by inputting a third plurality of training images included in the second training image group into the second neural network model.