  Few-shot learning is an interesting and challenging study, which enables
machines to learn from few samples like humans. Existing studies rarely exploit
auxiliary information from large amount of unlabeled data. Self-supervised
learning is emerged as an efficient method to utilize unlabeled data. Existing
self-supervised learning methods always rely on the combination of geometric
transformations for the single sample by augmentation, while seriously neglect
the endogenous correlation information among different samples that is the same
important for the task. In this work, we propose a Graph-driven Clustering
(GC), a novel augmentation-free method for self-supervised learning, which does
not rely on any auxiliary sample and utilizes the endogenous correlation
information among input samples. Besides, we propose Multi-pretext Attention
Network (MAN), which exploits a specific attention mechanism to combine the
traditional augmentation-relied methods and our GC, adaptively learning their
optimized weights to improve the performance and enabling the feature extractor
to obtain more universal representations. We evaluate our MAN extensively on
miniImageNet and tieredImageNet datasets and the results demonstrate that the
proposed method outperforms the state-of-the-art (SOTA) relevant methods.
