Cross Domain Few Shot Learning with Meta Fine Tuning
In this paper, we tackle the new Cross Domain Few Shot Learning benchmark proposed by the CVPR 2020 Challenge. To this end, we build upon state of the art methods in domain adaptation and few shot learning to create a system that can be trained to perform both tasks. Inspired by the need to create models designed to be fine tuned, we explore the integration of transfer learning (fine tuning) with meta learning algorithms, to train a network that has specific layers that are designed to be adapted at a later fine tuning stage. To do so, we modify the episodic training process to include a first order MAML based meta learning algorithm, and use a Graph Neural Network model as the subsequent meta learning module. We find that our proposed method helps to boost accuracy significantly, especially when combined with data augmentation. In our final results, we combine the novel method with the baseline method in a simple ensemble, and achieve an average accuracy of 73.78% on the benchmark. This is a 6.51% improvement over existing benchmarks that were trained solely on miniImagenet.
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