Cyclone Intensity Estimate with Context-Aware Cyclegan

Deep learning approaches to cyclone intensity estimation have recently shown promising results. However, suffering from the extreme scarcity of cyclone data on specific intensity, most existing deep learning methods fail to achieve satisfactory performance on cyclone intensity estimation, especially on classes with few instances. To avoid the degradation of recognition performance caused by scarce samples, we propose a context-aware CycleGAN which learns the latent evolution features from adjacent cyclone intensity and synthesizes CNN features of classes lacking samples from unpaired source classes. Specifically, our approach synthesizes features conditioned on the learned evolution features, while the extra information is not required. Experimental results of several evaluation methods show the effectiveness of our approach, even can predicting unseen classes.