Reinforced Self-Supervised Training for Few-Shot Learning

Few-shot learning is an open problem to learning a new concept with little supervision from limited labeled data. As an alternative knowledge for few-shot learning, self-supervised learning can extract supervisory signals directly from unlabeled data. However, existing self-supervised few-shot methods which directly take the summation of two tasks, have two fundamental bottlenecks: 1) representation bias: how to extract efficacious supervisory signals in self-supervision and eliminate the disturbance of undesirable shortcuts with limited examples and 2) objective conflict: how to adaptively trade-off the self-supervision and supervision to achieve the optimal model performance. To address the above problems, in this paper, we propose a novel approach named ReInforced SElf-supervised training (RISE) for few-shot learning. RISE leverages agent-relative supervision to eliminate the undesirable shortcut learning of self-supervised training. Meanwhile, it dynamically explores the balance between supervisory signals from self-supervised tasks and inherent supervision from few-shot tasks to avoid the trade-off dilemma. Therefore, the new pattern for training self-supervision can be resilient to few-shot learning and enhance the performance for few-shot identification. Extensive experiments on several public benchmark datasets verify the effectiveness of our approach.