- Abstract: Meta-learning has recently achieved success in many optimization problems. In general, a meta learner g(.) could be learned for a base model f(.) on a variety of tasks, such that it can be more efficient on a new task. In this paper, we make some key modifications to enhance the performance of meta-learning models. (1) we leverage different meta-strategies for different modules to optimize them separately: we use conservative “slow learners” on low-level basic feature representation layers and “fast learners” on high-level task-specific layers; (2) Furthermore, we provide theoretical analysis on why the proposed approach works, based on a case study on a two-layer MLP. We evaluate our model on synthetic MLP regression, as well as low-shot learning tasks on Omniglot and ImageNet benchmarks. We demonstrate that our approach is able to achieve state-of-the-art performance.
- Keywords: computer vision, meta learning
- TL;DR: We applied multiple meta-strategy to improve meta-learning performance on base CNNs.