Self-Supervision Can Be a Good Few-Shot Learner

• URL: http://arxiv.org/abs/2207.09176v1
• Date: Tue, 19 Jul 2022 10:23:40 GMT
• Title: Self-Supervision Can Be a Good Few-Shot Learner
• Authors: Yuning Lu, Liangjian Wen, Jianzhuang Liu, Yajing Liu, Xinmei Tian
• Abstract summary: We propose an effective unsupervised few-shot learning method, learning representations with self-supervision. Specifically, we maximize the mutual information (MI) of instances and their representations with a low-bias MI estimator. We show that self-supervised pre-training can outperform supervised pre-training under the appropriate conditions.
• Score: 42.06243069679068
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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