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Poster
How Fine-Tuning Allows for Effective Meta-Learning
Kurtland Chua · Qi Lei · Jason Lee

Fri Dec 10 08:30 AM -- 10:00 AM (PST) @ None #None
in Poster Session 8 »

Representation learning has served as a key tool for meta-learning, enabling rapid learning of new tasks. Recent works like MAML learn task-specific representations by finding an initial representation requiring minimal per-task adaptation (i.e. a fine-tuning-based objective). We present a theoretical framework for analyzing a MAML-like algorithm, assuming all available tasks require approximately the same representation. We then provide risk bounds on predictors found by fine-tuning via gradient descent, demonstrating that the method provably leverages the shared structure. We illustrate these bounds in the logistic regression and neural network settings. In contrast, we establish settings where learning one representation for all tasks (i.e. using a "frozen representation" objective) fails. Notably, any such algorithm cannot outperform directly learning the target task with no other information, in the worst case. This separation underscores the benefit of fine-tuning-based over “frozen representation” objectives in few-shot learning.

Keywords: Theory · Deep Learning · Optimization · Meta Learning · Representation Learning · Few Shot Learning

Author Information

Kurtland Chua (Princeton University)
Qi Lei (Princeton University)
Jason Lee (University of Southern California)

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