latent-world-recovery-for-multimodal-learning-with-missing-modalities-1feb809d·1 events·first seen Aliases: Latent World Recovery for Multimodal Learning with Missing Modalities
A new arXiv preprint introduces Latent World Recovery (LWR), a framework for multimodal learning when some modalities are unavailable at training or inference time. LWR aligns modality-specific embeddings in a shared latent space and fuses only available modalities, avoiding explicit reconstruction of missing ones. The approach is evaluated on incomplete multi-omics benchmarks for cancer phenotype classification and survival prediction, demonstrating robustness under partial observation.