  Existing few-shot learning (FSL) methods rely on training with a large
labeled dataset, which prevents them from leveraging abundant unlabeled data.
From an information-theoretic perspective, we propose an effective unsupervised
FSL method, learning representations with self-supervision. Following the
InfoMax principle, our method learns comprehensive representations by capturing
the intrinsic structure of the data. Specifically, we maximize the mutual
information (MI) of instances and their representations with a low-bias MI
estimator to perform self-supervised pre-training. Rather than supervised
pre-training focusing on the discriminable features of the seen classes, our
self-supervised model has less bias toward the seen classes, resulting in
better generalization for unseen classes. We explain that supervised
pre-training and self-supervised pre-training are actually maximizing different
MI objectives. Extensive experiments are further conducted to analyze their FSL
performance with various training settings. Surprisingly, the results show that
self-supervised pre-training can outperform supervised pre-training under the
appropriate conditions. Compared with state-of-the-art FSL methods, our
approach achieves comparable performance on widely used FSL benchmarks without
any labels of the base classes.
