1 code implementation • 18 Mar 2024 • Beichen Zhang, Xiaoxing Wang, Xiaohan Qin, Junchi Yan
In this work, we analyze the order-preserving ability on the whole search space (global) and a sub-space of top architectures (local), and empirically show that the local order-preserving for current two-stage NAS methods still need to be improved.
no code implementations • 5 Feb 2024 • Xiaoxing Wang, Jiaxing Li, Chao Xue, Wei Liu, Weifeng Liu, Xiaokang Yang, Junchi Yan, DaCheng Tao
BayesianOptimization(BO) is a sample-efficient black-box optimizer, and extensive methods have been proposed to build the absolute function response of the black-box function through a probabilistic surrogate model, including Tree-structured Parzen Estimator (TPE), random forest (SMAC), and Gaussian process (GP).
no code implementations • 21 Mar 2022 • Xiaoxing Wang, Jiale Lin, Junchi Yan, Juanping Zhao, Xiaokang Yang
In contrast, this paper introduces an efficient framework, named EAutoDet, that can discover practical backbone and FPN architectures for object detection in 1. 4 GPU-days.
Ranked #30 on Object Detection In Aerial Images on DOTA (using extra training data)
no code implementations • 10 Oct 2021 • Xiaoxing Wang, Wenxuan Guo, Junchi Yan, Jianlin Su, Xiaokang Yang
Also, we search on the search space of DARTS to compare with peer methods, and our discovered architecture achieves 97. 54% accuracy on CIFAR-10 and 75. 7% top-1 accuracy on ImageNet, which are state-of-the-art performance.
no code implementations • 30 Sep 2021 • Xiaoxing Wang, Xiangxiang Chu, Junchi Yan, Xiaokang Yang
Neural architecture search (NAS) has been an active direction of automatic machine learning (Auto-ML), aiming to explore efficient network structures.
no code implementations • ICCV 2023 • Xiaoxing Wang, Xiangxiang Chu, Yuda Fan, Zhexi Zhang, Bo Zhang, Xiaokang Yang, Junchi Yan
Albeit being a prevalent architecture searching approach, differentiable architecture search (DARTS) is largely hindered by its substantial memory cost since the entire supernet resides in the memory.
1 code implementation • ICLR 2021 • Xiangxiang Chu, Xiaoxing Wang, Bo Zhang, Shun Lu, Xiaolin Wei, Junchi Yan
We call this approach DARTS-.
Ranked #20 on Neural Architecture Search on NAS-Bench-201, CIFAR-10
5 code implementations • 16 May 2016 • Fengfu Li, Bin Liu, Xiaoxing Wang, Bo Zhang, Junchi Yan
We present a memory and computation efficient ternary weight networks (TWNs) - with weights constrained to +1, 0 and -1.