Conditional Self-Supervised Learning for Few-Shot Learning

Few-shot learning seeks to emulate humans by grasping a new concept with a few examples. However, it is often a tricky problem to completely learn a new concept and avoid falling into overfitting with few examples. Recently, self-supervision has been introduced as an auxiliary pretext task to enhance the generalization of few-shot learning models. However, in few-shot scenarios, self-supervised learning may easily learn undesirable shortcuts and biased representations due to insufficient information, negatively impacting the decision boundary of few-shot models. In this paper, we propose a new learning paradigm called conditional self-supervised learning (CSS), where the blind self-supervised tasks are constrained. Specifically, we first utilize the embedding knowledge of supervised learning as a condition to guide the representation learning process of self-supervised tasks. In order to optimize the overall objectives, we formulate the CSS as a multiple objective optimization problem to find Pareto optimal solutions. Moreover, we combine the meaningful information extracted from both supervised and self-supervised learning into a unified distribution, further enriching the original representation. Extensive experiments show that our method, without any fine-tuning, significantly improves accuracy in few-shot tasks compared to state-of-the-art methods.