Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning
"The pioneering unsupervised meta-learning work is a clustering-based pseudo-labeling method, which is model-agnostic and can utilize supervised algorithms for learning from unlabeled data. However, it often suffers from label inconsistency and limited diversity, which leads to poor performance. In this work, we prove that the core reason for this comes from the lack of a clustering-friendly property in the embedding space. Through comprehensive experimental validations, we break this restriction by minimizing the inter-class to intra-class similarity ratio to provide clustering-friendly embedding features. Surprisingly, we only utilize a simple clustering algorithm (k-means) on our embedding space to obtain pseudo-labels and achieve significant improvement. Moreover, we adopt a progressive evaluation mechanism to seek more diverse samples in order to further alleviate the limited diversity problem. Besides, our approach is model-agnostic and can easily be integrated into existing supervised methods. To demonstrate its generalization ability, we integrate it into two representative algorithms: MAML and EP. The results on three major few-shot benchmarks clearly show that the proposed method achieves significant improvement compared to the state-of-the-art models. Notably, our approach outperforms the corresponding supervised method in two tasks."