  Deep learning approaches to cyclone intensity estimationhave recently shown
promising results. However, sufferingfrom the extreme scarcity of cyclone data
on specific in-tensity, most existing deep learning methods fail to
achievesatisfactory performance on cyclone intensity estimation,especially on
classes with few instances. To avoid the degra-dation of recognition
performance caused by scarce samples,we propose a context-aware CycleGAN which
learns the la-tent evolution features from adjacent cyclone intensity
andsynthesizes CNN features of classes lacking samples fromunpaired source
classes. Specifically, our approach synthe-sizes features conditioned on the
learned evolution features,while the extra information is not required.
Experimentalresults of several evaluation methods show the effectivenessof our
approach, even can predicting unseen classes.
