Reproduction Report on "Learn to Pay Attention"

The model proposed in the "Learn to Pay Attention" paper introduced a novel way to generate a trainable attention module for convolutional neural networks. The paper demonstrated the attention modulein VGG-based and ResNet-based architectures, provided several options for implementation (includingthree options for layer depths at which attention modules are to be implemented, two options for calculating the compatibility between the global and local features in generating the attention maps, and two options for what method will be used to produce output probabilities from global-level feature vectors), described the dataset preprocessing and model training routines, and reported the results of the consequent models in several tasks. We have successfully implemented allpossible configurations of both VGG-based and ResNet-based attention models, and have replicated the paper's reported results in image classification and fine-grain recognition task using the (VGG-att2)-concat-pc configuration on the CIFAR-10 dataset and (VGG-att3)-concatpc configurationon CIFAR-100 and the SVHN dataset.