PRANCE: Joint Token-Optimization and Structural Channel-Pruning for Adaptive ViT Inference
Published in IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2025, 2025
Recommended citation: Ye Li, Chen Tang, Yuan Meng, Jiajun Fan, Zenghao Chai, Xinzhu Ma, Zhi Wang, Wenwu Zhu. "PRANCE: Joint Token-Optimization and Structural Channel-Pruning for Adaptive ViT Inference." TPAMI 2025. https://arxiv.org/abs/2407.05010
PRANCE is a Vision Transformer compression framework that jointly optimizes token usage and structural channel pruning, achieving state-of-the-art efficiency-accuracy trade-offs for adaptive ViT inference.