3.1.2. Model Pretraining

A fundamental step for FSC is pretraining the model on the base dataset to provide a suitable feature extractor G_θ. Specifically, the model is firstly trained with standard cross-entropy loss on the base dataset for all the classes to get the initialized model. Then, metric-based meta-learning is performed to continually train the model by building massive C-way K-shot tasks, finally outputting the pretrained model. This scheme can help the model improve its stability and generalization ability by imitating the few-shot settings that will be encountered in the target task. In fact, the proposed fine-tuning method in this study only uses the parameters of the pretrained model, which has nothing to do with the pretraining method. Thus, other pretraining methods based on different theories are also applicable.