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

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 rep