HCDS: Hierarchical Clustering for Cold-Start Few-Shot Data Selection

Deep learning models usually require large labeled datasets to generalize well, but this is computationally and financially costly. Cold-start few-shot data selection enables fast model generalization by selecting a few diverse, representative samples from an unlabeled data pool. To achieve this goal, previous work usually divides the training data into several clusters and performs sampling from these clusters. Yet, such a way tends to have two issues. First, imbalanced data distribution in the training data pool still exists in the selected subset, causing models' performance biases and suboptimal generalization ability. Second, these methods improve sample diversity in each cluster by considering either the feature dissimilarity among instances, or model uncertainty for individual instance. They ignore the entire representativeness of samples within a cluster. To tackle these challenges, we propose a novel framework HCDS : Hierarchical Clustering for Cold-Start Few-Shot Data Selection. Specifically, we first perform class-level clustering, using pseudo-labels for class supervision and applying contrastive clustering to derive class-rich features. We then refine these features within the class-level clusters into semantically meaningful features and perform representation-level clustering. Finally, we sample data from the representation-level clusters based on global similarity to ensure representativeness. Experimental results on six public datasets, including both balanced and imbalanced ones, show that HCDS achieves state-of-the-art performance, particularly with limited and imbalanced data.