Methods, apparatuses, and computer program products using a repeated convolution-based attention module for improved neural network implementations

Abstract

A method, apparatus, and computer program product are provided for providing improved neural network implementations using a repeated convolution-based attention module. Example embodiments implement a repeated convolution-based attention module that utilizes multiple iterations of a repeated convolutional application layer and subsequent augmentations to generate an attention module output. Example methods may include augmenting an attention input data object based on a previous iteration convolutional output to produce a current iteration input parameter, inputting the input parameter to a repeated convolutional application layer to generate a current iteration input parameter, repeating for multiple iterations, and augmenting the attention input data object based on the final convolutional output to produce an attention module output. Other methods may include an initial convolutional application layer, and/or apply and augment the output of the initial convolutional application layer, and include convolutional application layer(s) having at least two sub-layers.