stacked dilated convolutions

Stacked dilated convolutions refer to a type of convolutional neural network (CNN) architecture where multiple dilated convolutions are used in a series (or "stack") to extract features from the input data. Dilated convolutions are a type of convolution where the filter is applied to the input with a certain spacing between the filter elements, allowing the network to have a larger receptive field without increasing the number of parameters. By stacking multiple dilated convolutions, the network can learn more complex features and patterns from the input data, which can improve the accuracy of the model. This architecture has been used in various applications, including image segmentation, speech recognition, and natural language processing.