Decoder Choice Network for Meta-Learning
This work addresses a specific bottleneck in meta-learning for few-shot learning, offering an incremental improvement in parameter efficiency and performance.
The paper tackles the challenge of controlling gradient descent in meta-learning by limiting model parameters to a low-dimensional latent space, achieving superior performance on Omniglot and miniImageNet classification tasks.
Meta-learning has been widely used for implementing few-shot learning and fast model adaptation. One kind of meta-learning methods attempt to learn how to control the gradient descent process in order to make the gradient-based learning have high speed and generalization. This work proposes a method that controls the gradient descent process of the model parameters of a neural network by limiting the model parameters in a low-dimensional latent space. The main challenge of this idea is that a decoder with too many parameters is required. This work designs a decoder with typical structure and shares a part of weights in the decoder to reduce the number of the required parameters. Besides, this work has introduced ensemble learning to work with the proposed approach for improving performance. The results show that the proposed approach is witnessed by the superior performance over the Omniglot classification and the miniImageNet classification tasks.