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Deep neural networks (DNNs), including convolutional (CNNs) and residual (ResNets) models, are able to learn abstract representations from the input data by considering a deep hierarchy of layers that performs advanced feature extraction. The combination of these models with visual attention techniques can assist with the identification of the most representative parts of the data from a visual standpoint, obtained through a more detailed filtering of the features extracted by the operationaldoi:10.5281/zenodo.6414147 fatcat:gfid5sw3bvgglaefbvzjuyypr4