A Theory of Self-Supervised Framework for Few-Shot Learning
Recently, self-supervised learning (SSL) algorithms have been applied to Few-shot learning(FSL). FSL aims at distilling transferable knowledge on existing classes with large-scale labeled data to cope with novel classes for which only a few labeled data are available. Due to the limited number of novel classes, the initial embedding network becomes an essential component and can largely affect the performance in practice. But almost no one analyzes why a pre-trained embedding network with self-supervised training can provide representation for downstream FSL tasks in theory. In this paper, we first summarized the supervised FSL methods and explained why SSL is suitable for FSL. Then we further analyzed the main difference between supervised training and self-supervised training on FSL and obtained the bound for the gap between self-supervised loss and supervised loss. Finally, we proposed potential ways to improve the test accuracy under the setting of self-supervised FSL.
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