R-FUML models network outputs as fuzzy memberships, applies entropy-based robust multi-view fusion, and uses memory-effect isolation plus penalties to mitigate view conflicts, outperforming 15 baselines on eight datasets.
Reliable disentanglement multi-view learning against view adversarial attacks,
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Robust Fuzzy Multi-view Learning under View Conflict
R-FUML models network outputs as fuzzy memberships, applies entropy-based robust multi-view fusion, and uses memory-effect isolation plus penalties to mitigate view conflicts, outperforming 15 baselines on eight datasets.