11 citations · 23 across the 6 of their papers we have counts for
6 papers
EMQ: Evolving Training-free Proxies for Automated Mixed Precision Quantization
Peijie Dong, Lujun Li, Zimian Wei +3
Mixed-Precision Quantization~(MQ) can achieve a competitive accuracy-complexity trade-off for models. Conventional training-based search methods require time-consuming candidate tr…
DisWOT: Student Architecture Search for Distillation WithOut Training
Peijie Dong, Lujun Li, Zimian Wei
Knowledge distillation (KD) is an effective training strategy to improve the lightweight student models under the guidance of cumbersome teachers. However, the large architecture d…
Progressive Meta-Pooling Learning for Lightweight Image Classification Model
Peijie Dong, Xin Niu, Zhiliang Tian +5
Practical networks for edge devices adopt shallow depth and small convolutional kernels to save memory and computational cost, which leads to a restricted receptive field. Conventi…
RD-NAS: Enhancing One-shot Supernet Ranking Ability via Ranking Distillation from Zero-cost Proxies
Peijie Dong, Xin Niu, Lujun Li +5
Neural architecture search (NAS) has made tremendous progress in the automatic design of effective neural network structures but suffers from a heavy computational burden. One-shot…
DMFormer: Closing the Gap Between CNN and Vision Transformers
Zimian Wei, Hengyue Pan, Lujun Li +4
Vision transformers have shown excellent performance in computer vision tasks. As the computation cost of their self-attention mechanism is expensive, recent works tried to replace…
Prior-Guided One-shot Neural Architecture Search
Peijie Dong, Xin Niu, Lujun Li +5
Neural architecture search methods seek optimal candidates with efficient weight-sharing supernet training. However, recent studies indicate poor ranking consistency about the perf…