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Meta-Learning with Adaptive Hyperparameters
Sungyong Baik · Myungsub Choi · Janghoon Choi · Heewon Kim · Kyoung Mu Lee

Wed Dec 09 09:00 PM -- 11:00 PM (PST) @ Poster Session 4 #1199

Despite its popularity, several recent works question the effectiveness of MAML when test tasks are different from training tasks, thus suggesting various task-conditioned methodology to improve the initialization. Instead of searching for better task-aware initialization, we focus on a complementary factor in MAML framework, inner-loop optimization (or fast adaptation). Consequently, we propose a new weight update rule that greatly enhances the fast adaptation process. Specifically, we introduce a small meta-network that can adaptively generate per-step hyperparameters: learning rate and weight decay coefficients. The experimental results validate that the Adaptive Learning of hyperparameters for Fast Adaptation (ALFA) is the equally important ingredient that was often neglected in the recent few-shot learning approaches. Surprisingly, fast adaptation from random initialization with ALFA can already outperform MAML.

Author Information

Sungyong Baik (Seoul National University)
Myungsub Choi (Seoul National University)
Janghoon Choi (Seoul National University)
Heewon Kim (Seoul National University)
Kyoung Mu Lee (Seoul National University)