Inner layer hyperparametric meta-learning based on feature reuse

Few-shot learning requires fast learning and adaptation during the learning process, which has been seen as a very challenging problem because of its stringent training requirements. With the development of deep learning, meta-learning is increasingly applied to solve few-shot problems. Model-agnostic meta-learning (MAML) is applied to model-agnostic tasks by finding common initialization parameters applicable to the model through both inner and outer loops. However, the generalization performance of MAML is not strong, and its inner loop is not fully functional. Therefore, we add learnable hyperparameters to the inner loop to make the model training results more relevant to the current task and reduce the number of layers of gradient updates without unnecessary computation. Experimental results show that the inner loop hyperparameter learning based on feature reuse (HLFR), finds better initialization parameters compared to MAML.