Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters

16 Jun 2020Kaiyi JiJason D. LeeYingbin LiangH. Vincent Poor

Although model-agnostic meta-learning (MAML) is a very successful algorithm in meta-learning practice, it can have high computational cost because it updates all model parameters over both the inner loop of task-specific adaptation and the outer-loop of meta initialization training. A more efficient algorithm ANIL (which refers to almost no inner loop) was proposed recently by Raghu et al. 2019, which adapts only a small subset of parameters in the inner loop and thus has substantially less computational cost than MAML as demonstrated by extensive experiments... (read more)

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