How Fine-Tuning Allows for Effective Meta-Learning

Understand

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.