The Research about Recurrent Model-Agnostic Meta Learning

Although Deep Neural Networks (DNNs) have performed great success in machine learning domain, they usually show poorly on few-shot learning tasks, where a classifier has to quickly generalize after getting very few samples from each class. A Model-Agnostic Meta Learning (MAML) model, which is able to solve new learning tasks, only using a small number of training data. A MAML model with a Convolutional Neural Network (CNN) architecture is implemented as well, trained on the Omniglot dataset (rather than DNN), as a baseline for image classification tasks. However, our baseline model suffered from a long-period training process and relatively low efficiency. To address these problems, we introduced Recurrent Neural Network (RNN) architecture and its advanced variants into our MAML model, including Long Short-Term Memory (LSTM) architecture and its variants: LSTM-b and Gated Recurrent Unit (GRU). The experiment results, measured by ac- curacies, demonstrate a considerable improvement in image classification performance and training efficiency compared to the baseline models.