Accelerating Meta-Learning with the Enhanced Reptile Algorithm for Rapid Adaptation in Neural Networks

Abstract

Reptile is an innovative meta-learning approach that improves the process of neural networks training across diverse tasks. Reptile differs from the preference gradient strategies. It searches for a model weight vector which can enable a model to learn a new set of tasks with a small number of weight updates. This is accomplished via a first-order optimization process which makes it less intricate than other strategies like model-agnostic meta-learning MAML. Reptile is said to sample a number of tasks and then trains the model on each of the tasks via performing a series of a few gradient steps, systematically updating the model towards the average gradient direction across all the tasks. This enables the model to generalize well with new tasks trained with few iterations, thus proving beneficial in few-shot learning. Reptile enables faster adaptation of the model by focusing on the learning of better initial parameters thereby lowering computational overhead and the training duration. The algorithm is noted for its ability not only to learn but also to adapt itself to new tasks in a short period of time, which greatly extends the scope of its application, especially in areas where data is scarce. Examples of these areas are robotics, personalized advertisements, and decision-making approaches that need to operate in real time. Reptile Algorithm builds good on gradient-based approaches and can spearhead volumetric applications of meta reasoning by being exceptionally efficient, scalable and less computationally intensive.