How Fine-Tuning Allows for Effective Meta-Learning
This work addresses the theoretical underpinnings of meta-learning for researchers, offering insights into the benefits of fine-tuning in few-shot learning, though it is incremental as it builds on existing methods like MAML.
The paper tackles the problem of understanding why fine-tuning-based meta-learning methods like MAML work effectively by providing a theoretical framework that analyzes representations and proves risk bounds for fine-tuning via gradient descent, showing it can leverage shared task structure, with a separation result demonstrating superiority over frozen representation methods in worst-case scenarios.
Representation learning has been widely studied in the context of meta-learning, enabling rapid learning of new tasks through shared representations. Recent works such as MAML have explored using fine-tuning-based metrics, which measure the ease by which fine-tuning can achieve good performance, as proxies for obtaining representations. We present a theoretical framework for analyzing representations derived from a MAML-like algorithm, assuming the available tasks use approximately the same underlying representation. We then provide risk bounds on the best predictor found by fine-tuning via gradient descent, demonstrating that the algorithm can provably leverage the shared structure. The upper bound applies to general function classes, which we demonstrate by instantiating the guarantees of our framework in the logistic regression and neural network settings. In contrast, we establish the existence of settings where any algorithm, using a representation trained with no consideration for task-specific fine-tuning, performs as well as a learner with no access to source tasks in the worst case. This separation result underscores the benefit of fine-tuning-based methods, such as MAML, over methods with "frozen representation" objectives in few-shot learning.