  The taxonomy of galaxy morphology is critical in astrophysics as the
morphological properties are powerful tracers of galaxy evolution. With the
upcoming Large-scale Imaging Surveys, billions of galaxy images challenge
astronomers to accomplish the classification task by applying traditional
methods or human inspection. Consequently, machine learning, in particular
supervised deep learning, has been widely employed to classify galaxy
morphologies recently due to its exceptional automation, efficiency, and
accuracy. However, supervised deep learning requires extensive training sets,
which causes considerable workloads; also, the results are strongly dependent
on the characteristics of training sets, which leads to biased outcomes
potentially. In this study, we attempt Few-shot Learning to bypass the two
issues. Our research adopts the dataset from Galaxy Zoo Challenge Project on
Kaggle, and we divide it into five categories according to the corresponding
truth table. By classifying the above dataset utilizing few-shot learning based
on Siamese Networks and supervised deep learning based on AlexNet, VGG_16, and
ResNet_50 trained with different volumes of training sets separately, we find
that few-shot learning achieves the highest accuracy in most cases, and the
most significant improvement is $21\%$ compared to AlexNet when the training
sets contain 1000 images. In addition, to guarantee the accuracy is no less
than 90\%, few-shot learning needs $\sim$6300 images for training, while
ResNet_50 requires 13000 images. Considering the advantages stated above,
foreseeably, few-shot learning is suitable for the taxonomy of galaxy
morphology and even for identifying rare astrophysical objects, despite limited
training sets consisting of observational data only.
