420 citations · 902 across the 15 of their papers we have counts for
6 papers · 1 filter
Weight-Sharing Neural Architecture Search: A Battle to Shrink the Optimization Gap
Lingxi Xie, Xin Chen, Kaifeng Bi +8
Neural architecture search (NAS) has attracted increasing attentions in both academia and industry. In the early age, researchers mostly applied individual search methods which sam…
GOLD-NAS: Gradual, One-Level, Differentiable
Kaifeng Bi, Lingxi Xie, Xin Chen +2
There has been a large literature of neural architecture search, but most existing work made use of heuristic rules that largely constrained the search flexibility. In this paper,…
Fitting the Search Space of Weight-sharing NAS with Graph Convolutional Networks
Xin Chen, Lingxi Xie, Jun Wu +3
Neural architecture search has attracted wide attentions in both academia and industry. To accelerate it, researchers proposed weight-sharing methods which first train a super-netw…
Circumventing Outliers of AutoAugment with Knowledge Distillation
Longhui Wei, An Xiao, Lingxi Xie +3
AutoAugment has been a powerful algorithm that improves the accuracy of many vision tasks, yet it is sensitive to the operator space as well as hyper-parameters, and an improper se…
Progressive DARTS: Bridging the Optimization Gap for NAS in the Wild
Xin Chen, Lingxi Xie, Jun Wu +1
With the rapid development of neural architecture search (NAS), researchers found powerful network architectures for a wide range of vision tasks. However, it remains unclear if th…
Latency-Aware Differentiable Neural Architecture Search
Yuhui Xu, Lingxi Xie, Xiaopeng Zhang +4
Differentiable neural architecture search methods became popular in recent years, mainly due to their low search costs and flexibility in designing the search space. However, these…