  Social media plays a significant role in sharing essential information, which
helps humanitarian organizations in rescue operations during and after disaster
incidents. However, developing an efficient method that can provide rapid
analysis of social media images in the early hours of disasters is still
largely an open problem, mainly due to the lack of suitable datasets and the
sheer complexity of this task. In addition, supervised methods can not
generalize well to novel disaster incidents. In this paper, inspired by the
success of Knowledge Distillation (KD) methods, we propose an unsupervised deep
neural network to detect and localize damages in social media images. Our
proposed KD architecture is a feature-based distillation approach that
comprises a pre-trained teacher and a smaller student network, with both
networks having similar GAN architecture containing a generator and a
discriminator. The student network is trained to emulate the behavior of the
teacher on training input samples, which, in turn, contain images that do not
include any damaged regions. Therefore, the student network only learns the
distribution of no damage data and would have different behavior from the
teacher network-facing damages. To detect damage, we utilize the difference
between features generated by two networks using a defined score function that
demonstrates the probability of damages occurring. Our experimental results on
the benchmark dataset confirm that our approach outperforms state-of-the-art
methods in detecting and localizing the damaged areas, especially for novel
disaster types.
