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Resilience and Plasticity of Deep Network Interpretations for Aerial Imagery
This paper aims at visualizing deep convolutional neural network interpretations for aerial imagery and understanding how these interpretations change across datasets or when network weights are damaged. Our visualization results offer insights on the generalization power and resilience of commonly used networks, such as VGG16, ResNet50, and DenseNet121. Our experiments on the AID and the UCM aerial datasets demonstrate the emergence of object and texture detectors in convolutional networksdoi:10.1109/access.2020.3008323 fatcat:rugyharb6jg4vfrmvtwar6hv7y