  Few-shot learning is challenging due to the limited data and labels. Existing
algorithms usually resolve this problem by pre-training the model with a
considerable amount of annotated data which shares knowledge with the target
domain. Nevertheless, large quantities of homogenous data samples are not
always available. To tackle this issue, we develop a framework that enables the
model to surf the Internet, which implies that the model can collect and
annotate data without manual effort. Since the online data is virtually
limitless and continues to be generated, the model can thus be empowered to
constantly obtain up-to-date knowledge from the Internet. Additionally, we
observe that the generalization ability of the learned representation is
crucial for self-supervised learning. To present its importance, a naive yet
efficient normalization strategy is proposed. Consequentially, this strategy
boosts the accuracy of the model significantly (20.46% at most). We demonstrate
the superiority of the proposed framework with experiments on miniImageNet,
tieredImageNet and Omniglot. The results indicate that our method has surpassed
previous unsupervised counterparts by a large margin (more than 10%) and
obtained performance comparable with the supervised ones.
