# Cyclone intensity estimate with context-aware cyclegan

## TL;DR

This paper introduces a context-aware CycleGAN model that improves cyclone intensity estimation by synthesizing features for underrepresented classes, leveraging latent evolution features without extra information, and demonstrating effectiveness on unseen classes.

## Contribution

The paper presents a novel context-aware CycleGAN that learns evolution features to synthesize data for scarce cyclone intensity classes, enhancing prediction performance.

## Key findings

- Effective in predicting unseen cyclone classes
- Improves accuracy on classes with few samples
- Demonstrates robustness across evaluation methods

## Abstract

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.