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Auto-Train-Once: Controller Network Guided Automatic Network Pruning from Scratch
March 25, 2024, 4:42 a.m. | Xidong Wu, Shangqian Gao, Zeyu Zhang, Zhenzhen Li, Runxue Bao, Yanfu Zhang, Xiaoqian Wang, Heng Huang
cs.LG updates on arXiv.org arxiv.org
Abstract: Current techniques for deep neural network (DNN) pruning often involve intricate multi-step processes that require domain-specific expertise, making their widespread adoption challenging. To address the limitation, the Only-Train-Once (OTO) and OTOv2 are proposed to eliminate the need for additional fine-tuning steps by directly training and compressing a general DNN from scratch. Nevertheless, the static design of optimizers (in OTO) can lead to convergence issues of local optima. In this paper, we proposed the Auto-Train-Once (ATO), …
abstract adoption arxiv auto cs.cv cs.lg current deep neural network dnn domain expertise fine-tuning making network neural network processes pruning scratch train training type
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