  Few-shot learning is the process of learning novel classes using only a few
examples and it remains a challenging task in machine learning. Many
sophisticated few-shot learning algorithms have been proposed based on the
notion that networks can easily overfit to novel examples if they are simply
fine-tuned using only a few examples. In this study, we show that in the
commonly used low-resolution mini-ImageNet dataset, the fine-tuning method
achieves higher accuracy than common few-shot learning algorithms in the 1-shot
task and nearly the same accuracy as that of the state-of-the-art algorithm in
the 5-shot task. We then evaluate our method with more practical tasks, namely
the high-resolution single-domain and cross-domain tasks. With both tasks, we
show that our method achieves higher accuracy than common few-shot learning
algorithms. We further analyze the experimental results and show that: 1) the
retraining process can be stabilized by employing a low learning rate, 2) using
adaptive gradient optimizers during fine-tuning can increase test accuracy, and
3) test accuracy can be improved by updating the entire network when a large
domain-shift exists between base and novel classes.
