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 representations and sub...
Sooin Kim, Kyungtae Kim, Donghoon Kim et al.
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