1 code implementation • 29 Nov 2022 • Chuming Li, Jie Liu, Yinmin Zhang, Yuhong Wei, Yazhe Niu, Yaodong Yang, Yu Liu, Wanli Ouyang
In the learning phase, each agent minimizes the TD error that is dependent on how the subsequent agents have reacted to their chosen action.
Ranked #1 on SMAC on SMAC 3s5z_vs_3s6z
no code implementations • NeurIPS 2021 • Yifei Wang, Zhengyang Geng, Feng Jiang, Chuming Li, Yisen Wang, Jiansheng Yang, Zhouchen Lin
Multi-view methods learn representations by aligning multiple views of the same image and their performance largely depends on the choice of data augmentation.
1 code implementation • ICCV 2021 • BoYu Chen, Peixia Li, Baopu Li, Chen Lin, Chuming Li, Ming Sun, Junjie Yan, Wanli Ouyang
We present BN-NAS, neural architecture search with Batch Normalization (BN-NAS), to accelerate neural architecture search (NAS).
no code implementations • 7 Aug 2021 • BoYu Chen, Peixia Li, Baopu Li, Chuming Li, Lei Bai, Chen Lin, Ming Sun, Junjie Yan, Wanli Ouyang
Then, a compact set of the possible combinations for different token pooling and attention sharing mechanisms are constructed.
2 code implementations • ICCV 2021 • BoYu Chen, Peixia Li, Chuming Li, Baopu Li, Lei Bai, Chen Lin, Ming Sun, Junjie Yan, Wanli Ouyang
We introduce the first Neural Architecture Search (NAS) method to find a better transformer architecture for image recognition.
Ranked #446 on Image Classification on ImageNet
no code implementations • ICCV 2021 • Jiaheng Liu, Yudong Wu, Yichao Wu, Chuming Li, Xiaolin Hu, Ding Liang, Mengyu Wang
To estimate the LID of each face image in the verification process, we propose two types of LID Estimation (LIDE) methods, which are reference-based and learning-based estimation methods, respectively.
1 code implementation • CVPR 2021 • Jie Liu, Chuming Li, Feng Liang, Chen Lin, Ming Sun, Junjie Yan, Wanli Ouyang, Dong Xu
To develop a practical method for learning complex inception convolution based on the data, a simple but effective search algorithm, referred to as efficient dilation optimization (EDO), is developed.
1 code implementation • 12 Dec 2020 • Matthieu Lin, Chuming Li, Xingyuan Bu, Ming Sun, Chen Lin, Junjie Yan, Wanli Ouyang, Zhidong Deng
Furthermore, the bipartite match of ED harms the training efficiency due to the large ground truth number in crowd scenes.
no code implementations • 21 Oct 2020 • Jie Liu, Chen Lin, Chuming Li, Lu Sheng, Ming Sun, Junjie Yan, Wanli Ouyang
Several variants of stochastic gradient descent (SGD) have been proposed to improve the learning effectiveness and efficiency when training deep neural networks, among which some recent influential attempts would like to adaptively control the parameter-wise learning rate (e. g., Adam and RMSProp).
1 code implementation • ICCV 2021 • Mingzhu Shen, Feng Liang, Ruihao Gong, Yuhang Li, Chuming Li, Chen Lin, Fengwei Yu, Junjie Yan, Wanli Ouyang
Therefore, we propose to combine Network Architecture Search methods with quantization to enjoy the merits of the two sides.
no code implementations • 28 Sep 2020 • Mingzhu Shen, Feng Liang, Chuming Li, Chen Lin, Ming Sun, Junjie Yan, Wanli Ouyang
Automatic search of Quantized Neural Networks (QNN) has attracted a lot of attention.
no code implementations • ECCV 2020 • Ronghao Guo, Chen Lin, Chuming Li, Keyu Tian, Ming Sun, Lu Sheng, Junjie Yan
Specifically, the difficulties for architecture searching in such a complex space has been eliminated by the proposed stabilized share-parameter proxy, which employs Stochastic Gradient Langevin Dynamics to enable fast shared parameter sampling, so as to achieve stabilized measurement of architecture performance even in search space with complex topological structures.
no code implementations • CVPR 2020 • Xiang Li, Chen Lin, Chuming Li, Ming Sun, Wei Wu, Junjie Yan, Wanli Ouyang
In this paper, we analyse existing weight sharing one-shot NAS approaches from a Bayesian point of view and identify the posterior fading problem, which compromises the effectiveness of shared weights.
1 code implementation • ICCV 2019 • Chuming Li, Yuan Xin, Chen Lin, Minghao Guo, Wei Wu, Wanli Ouyang, Junjie Yan
The key contribution of this work is the design of search space which can guarantee the generalization and transferability on different vision tasks by including a bunch of existing prevailing loss functions in a unified formulation.
1 code implementation • ICCV 2019 • Chen Lin, Minghao Guo, Chuming Li, Yuan Xin, Wei Wu, Dahua Lin, Wanli Ouyang, Junjie Yan
Data augmentation is critical to the success of modern deep learning techniques.