MAANU-NET: Multi-Level Attention and Atrous Pyramid Nested U-Net For Wrecked Objects Segmentation in Forward-Looking Sonar Images

Thinet is a recent method for pruning convolutional neural networks. This method uses a norm of a subset of the components of the output resulting from the convolutional layer succeed- ing the layer from which the filters are to be removed for pruning the network. The Thinet algorithm is very time-consuming, in view of the fact that the filters for removal are selected one by one iteratively. in this paper, we propose a modified version of Thinet, in which the same information on the output of the same convolutional layer as used by Thinet is employed to select all the filters together in a single step, for pruning the network. The proposed modified algorithm is shown to have a time-complexity that is only a small fraction of that of Thinet or any other state-of-the-art algorithm and that the pruned network has almost the same reduction in its accuracy as that of the network pruned by Thinet.