Gradient Based Meta Learning

Gradient-based meta-learning aims to develop algorithms that learn to learn, rapidly adapting to new tasks with minimal data by leveraging previously acquired knowledge. Current research focuses on improving the efficiency and robustness of these methods, addressing issues like high variance in gradient estimates, computational cost of higher-order derivatives, and the challenge of out-of-distribution generalization. This is achieved through techniques such as gradient augmentation, Hessian suppression, and adaptive weighting of losses, often applied within model-agnostic meta-learning (MAML) frameworks or adapted for specific architectures like Transformers and spiking neural networks. The resulting advancements have significant implications for few-shot learning and other areas requiring rapid adaptation to new data, including personalized recommendations and on-device model adaptation.