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cs.CV2026
When Does Sparse MoE Help in Vision? The Role of Backbone Compute Leverage in Sparse Routing
Libo Sun, Po-wei Harn, Peixiong He +1
Mixture-of-Experts (MoE) networks promise favorable accuracy-compute trade-offs, yet practical vision deployments are hindered by expert collapse and limited end-to-end efficiency…
cs.CV2026
FineRMoE: Dimension Expansion for Finer-Grained Expert with Its Upcycling Approach
Ning Liao, Xiaoxing Wang, Xiaohan Qin +1
As revealed by the scaling law of fine-grained MoE, model performance ceases to be improved once the granularity of the intermediate dimension exceeds the optimal threshold, limiti…
cs.CV2024
Boosting Order-Preserving and Transferability for Neural Architecture Search: a Joint Architecture Refined Search and Fine-tuning Approach
Beichen Zhang, Xiaoxing Wang, Xiaohan Qin +1
Supernet is a core component in many recent Neural Architecture Search (NAS) methods. It not only helps embody the search space but also provides a (relative) estimation of the fin…