Exploring Diffusion Transformers for Cross-Modal Augmentation in Multimodal Brain State Decoding

arXiv:2609.11341 · cs.AI · Submitted 2026-09-10 · Read on arXiv

cs.AI

Submitted: 2026-09-10

Updated: 2026-09-10

Comments: CoMA-DiT, a cross-modal augmentation framework built on Diffusion Transformer, extends multimodal learning beyond fusion by leveraging paired modalities as mutual generative supervision to enrich training data and improve brain state decoding

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Transformer — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Generalizability — 1× in this paper · explained in 6 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Multimodal Learning — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes

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