SWAN: World-Aware Adaptive Multimodal Networks for Runtime Variations
Multimodal deep neural networks deployed in realistic environments must contend with runtime variations: changes in modality quality, overall input complexity, and available platform resources. Current networks struggle with such fluctuations -- adaptive networks cannot adhere to a strict compute budget, controller-based networks neglect to consider input complexity, and statically provisioned networks fail at all the above. Consequently, they do not extract maximum utility from the expended computational resources. We present SWAN (Sample and World-Aware Multimodal Network), the first adaptive multimodal network that accomplishes all three goals. SWAN employs a quality-aware controller t...
| Authors | Jason Wu, Shir-Kang Scott Jin, Yuyang Yuan et al. |
|---|---|
| Primary category | cs.LG |
| Published | 28 April 2026 |
| Last updated at source | 1 May 2026 |
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