Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
It addresses the challenge of data-efficient learning across diverse tasks like classification, regression, and reinforcement learning, representing a novel method rather than an incremental improvement.
The paper tackles the problem of enabling deep networks to quickly adapt to new tasks with minimal data by proposing a model-agnostic meta-learning algorithm that trains model parameters for easy fine-tuning, achieving state-of-the-art performance on few-shot image classification benchmarks and good results in regression and reinforcement learning.
We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning. The goal of meta-learning is to train a model on a variety of learning tasks, such that it can solve new learning tasks using only a small number of training samples. In our approach, the parameters of the model are explicitly trained such that a small number of gradient steps with a small amount of training data from a new task will produce good generalization performance on that task. In effect, our method trains the model to be easy to fine-tune. We demonstrate that this approach leads to state-of-the-art performance on two few-shot image classification benchmarks, produces good results on few-shot regression, and accelerates fine-tuning for policy gradient reinforcement learning with neural network policies.