Few-shot Fish Image Generation and Classification

Scientific studies on fishes have considerable importance to the fishery industry and marine ecosystem. Fish images are typically collected by scuba divers or autonomous underwater vehicles and then annotated manually by marine biologists. In recent years, with the rise of deep learning, the field of image classification has been greatly improved. But, fish image classification which can be considered as fine-grained problem is more challenging than common image classification. Usually, fish images are low-quality and belong to small-scale data, while classic CNN models require a large quantity of high-quality data to gain excellent results. For few-shot fish images, it's hard to get rich diversity by rotation, slight transform, and upsampling. In this paper, our method could obtain few-shot classes which are translated from large-scale sample classes, while maintaining the intra-class similarity, diversity and inter-class identifiabilityso as to improve the classification effect of fish image.