Timezone: »
Neural architecture search (NAS) is inherently subject to the gap of architectures during searching and validating. To bridge this gap, we develop Differentiable ArchiTecture Approximation (DATA) with an Ensemble Gumbel-Softmax (EGS) estimator to automatically approximate architectures during searching and validating in a differentiable manner. Technically, the EGS estimator consists of a group of Gumbel-Softmax estimators, which is capable of converting probability vectors to binary codes and passing gradients from binary codes to probability vectors. Benefiting from such modeling, in searching, architecture parameters and network weights in the NAS model can be jointly optimized with the standard back-propagation, yielding an end-to-end learning mechanism for searching deep models in a large enough search space. Conclusively, during validating, a high-performance architecture that approaches to the learned one during searching is readily built. Extensive experiments on a variety of popular datasets strongly evidence that our method is capable of discovering high-performance architectures for image classification, language modeling and semantic segmentation, while guaranteeing the requisite efficiency during searching.
Author Information
Jianlong Chang (National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences)
xinbang zhang (Institute of Automation,Chinese Academy of Science)
Yiwen Guo (Bytedance AI Lab)
GAOFENG MENG (Institute of Automation, Chinese Academy of Sciences)
SHIMING XIANG (Chinese Academy of Sciences, China)
Chunhong Pan (Institute of Automation, Chinese Academy of Sciences)
More from the Same Authors
-
2021 Spotlight: Robust and Fully-Dynamic Coreset for Continuous-and-Bounded Learning (With Outliers) Problems »
Zixiu Wang · Yiwen Guo · Hu Ding -
2022 Poster: When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture »
Yichuan Mo · Dongxian Wu · Yifei Wang · Yiwen Guo · Yisen Wang -
2022 Spotlight: Lightning Talks 6A-2 »
Yichuan Mo · Botao Yu · Gang Li · Zezhong Xu · Haoran Wei · Arsene Fansi Tchango · Raef Bassily · Haoyu Lu · Qi Zhang · Songming Liu · Mingyu Ding · Peiling Lu · Yifei Wang · Xiang Li · Dongxian Wu · Ping Guo · Wen Zhang · Hao Zhongkai · Mehryar Mohri · Rishab Goel · Yisen Wang · Yifei Wang · Yangguang Zhu · Zhi Wen · Ananda Theertha Suresh · Chengyang Ying · Yujie Wang · Peng Ye · Rui Wang · Nanyi Fei · Hui Chen · Yiwen Guo · Wei Hu · Chenglong Liu · Julien Martel · Yuqi Huo · Wu Yichao · Hang Su · Yisen Wang · Peng Wang · Huajun Chen · Xu Tan · Jun Zhu · Ding Liang · Zhiwu Lu · Joumana Ghosn · Shanshan Zhang · Wei Ye · Ze Cheng · Shikun Zhang · Tao Qin · Tie-Yan Liu -
2022 Spotlight: When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture »
Yichuan Mo · Dongxian Wu · Yifei Wang · Yiwen Guo · Yisen Wang -
2021 Poster: Robust and Fully-Dynamic Coreset for Continuous-and-Bounded Learning (With Outliers) Problems »
Zixiu Wang · Yiwen Guo · Hu Ding -
2020 Poster: Backpropagating Linearly Improves Transferability of Adversarial Examples »
Yiwen Guo · Qizhang Li · Hao Chen -
2020 Poster: Practical No-box Adversarial Attacks against DNNs »
Qizhang Li · Yiwen Guo · Hao Chen -
2019 Poster: DetNAS: Backbone Search for Object Detection »
Yukang Chen · Tong Yang · Xiangyu Zhang · GAOFENG MENG · Xinyu Xiao · Jian Sun -
2019 Poster: Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks »
Yiwen Guo · Ziang Yan · Changshui Zhang -
2018 Poster: Structure-Aware Convolutional Neural Networks »
Jianlong Chang · Jie Gu · Lingfeng Wang · GAOFENG MENG · SHIMING XIANG · Chunhong Pan