Feature-Refinement Classification Head (FCH) for Modeling Inter-Sample Relationships in Neural Networks

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Conventional classification methods rely on the assumption that samples are independent and identically distributed, often ignoring the latent relational structures governing real-world data. Consequently, these methods fail to capture cross-sample dependencies, resulting in impoverished feature representations and suboptimal generalization. To address this limitation, we propose the Feature-refinement Classification Head (FCH), a novel and modular component that explicitly models inter-sample relationships within training batches. FCH constructs an adjacency matrix from embedding vectors and employs a graph neural network to refine features via relational propagation. Seamlessly integrated and architecture-agnostic, FCH leverages the end-to-end optimization using cross-entropy and structure preserving loss as joint objectives. Extensive experiments on benchmarks such as CIFAR-10, MNIST, and STL-10 demonstrate that FCH consistently enhances accuracy, precision, recall, and F1-scores across diverse backbones. Notably, FCH demonstrates significant performance improvements even in lightweight models, demonstrating its robustness, scalability, and practicality. Overall, FCH offers a principled, generalizable alternative to traditional classification heads by effectively leveraging sample-to-sample relationships.