Diversify and Match

 We propose a novel unsupervised domain adaptation method for object detection. We aim to alleviate limitations of feature-level and pixel-level domain adaptation approaches. Our approach is composed of various style translation and robust intra-class feature learning, and we construct a structured domain adaptation framework. Our method outperforms the state-of-the-art methods by a large margin in terms of mean average precision (mAP) on cartoon datasets.

• Domain adaptation framework

 Our proposed domain adaptation framework is composed of various style translation module, object detection module, and domain discriminator module. Various style translation module perturbs the input image to arbitrary cartoon styles. The translated source image is utilized to learn the large intra-class variance through domain discriminator. This scheme encourages the network to generate less domain-specific and more semantic features. Finally, these features are used for effective object detection on the target domain.