Revisiting Fine-tuning for Few-shot Learning

A BSTRACT 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. 1 I NTRODUCTION Previous studies have shown that high image classification performance can be achieved by using deep networks and big datasets (Krizhevsky et al., 2012; Simonyan & Zisserman, 2015; He et al., 2016; Szegedy et al., 2015). However, the performances of these algorithms rely heavily on extensive manually annotated images, and considerable cost is often incurred in preparing these datasets. To avoid this problem, few-shot learning, which is a task of learning novel classes using only a few examples, has been actively researched.