no code implementations • 25 Mar 2024 • Lin Zhao, Tianchen Zhao, Zinan Lin, Xuefei Ning, Guohao Dai, Huazhong Yang, Yu Wang
In recent years, there has been significant progress in the development of text-to-image generative models.
no code implementations • ICCV 2023 • Tianchen Zhao, Xuefei Ning, Ke Hong, Zhongyuan Qiu, Pu Lu, Yali Zhao, Linfeng Zhang, Lipu Zhou, Guohao Dai, Huazhong Yang, Yu Wang
One reason for this high resource consumption is the presence of a large number of redundant background points in Lidar point clouds, resulting in spatial redundancy in both 3D voxel and dense BEV map representations.
1 code implementation • 2 Feb 2023 • Junbo Zhao, Xuefei Ning, Enshu Liu, Binxin Ru, Zixuan Zhou, Tianchen Zhao, Chen Chen, Jiajin Zhang, Qingmin Liao, Yu Wang
In the first step, we train different sub-predictors on different types of available low-fidelity information to extract beneficial knowledge as low-fidelity experts.
no code implementations • 11 Aug 2022 • Tianchen Zhao, James Stokes, Shravan Veerapaneni
Variational optimization of neural-network representations of quantum states has been successfully applied to solve interacting fermionic problems.
no code implementations • CVPR 2022 • Tianchen Zhao, Niansong Zhang, Xuefei Ning, He Wang, Li Yi, Yu Wang
We propose CodedVTR (Codebook-based Voxel TRansformer), which improves data efficiency and generalization ability for 3D sparse voxel transformers.
no code implementations • 1 Jan 2021 • Kai Zhong, Xuefei Ning, Tianchen Zhao, Zhenhua Zhu, Shulin Zeng, Guohao Dai, Yu Wang, Huazhong Yang
Through this dynamic precision framework, we can reduce the bit-width of convolution, which is the most computational cost, while keeping the training process close to the full precision floating-point training.
1 code implementation • 22 Dec 2020 • Xuefei Ning, Junbo Zhao, Wenshuo Li, Tianchen Zhao, Yin Zheng, Huazhong Yang, Yu Wang
In this paper, considering scenarios with capacity budget, we aim to discover adversarially robust architecture at targeted capacities.
1 code implementation • ICCV 2021 • Tianchen Zhao, Xiang Xu, Mingze Xu, Hui Ding, Yuanjun Xiong, Wei Xia
We propose a new method to detect deepfake images using the cue of the source feature inconsistency within the forged images.
1 code implementation • 25 Nov 2020 • Xuefei Ning, Changcheng Tang, Wenshuo Li, Songyi Yang, Tianchen Zhao, Niansong Zhang, Tianyi Lu, Shuang Liang, Huazhong Yang, Yu Wang
Neural Architecture Search (NAS) has received extensive attention due to its capability to discover neural network architectures in an automated manner.
no code implementations • 21 Nov 2020 • Tianchen Zhao, Xuefei Ning, Xiangsheng Shi, Songyi Yang, Shuang Liang, Peng Lei, Jianfei Chen, Huazhong Yang, Yu Wang
We also design the micro-level search space to strengthen the information flow for BNN.
no code implementations • 20 Nov 2020 • Tianchen Zhao, James Stokes, Oliver Knitter, Brian Chen, Shravan Veerapaneni
An identification is found between meta-learning and the problem of determining the ground state of a randomly generated Hamiltonian drawn from a known ensemble.
no code implementations • 28 Sep 2020 • Xuefei Ning, Wenshuo Li, Zixuan Zhou, Tianchen Zhao, Shuang Liang, Yin Zheng, Huazhong Yang, Yu Wang
A major challenge in NAS is to conduct a fast and accurate evaluation of neural architectures.
no code implementations • 4 Jun 2020 • Kai Zhong, Xuefei Ning, Guohao Dai, Zhenhua Zhu, Tianchen Zhao, Shulin Zeng, Yu Wang, Huazhong Yang
For training a variety of models on CIFAR-10, using 1-bit mantissa and 2-bit exponent is adequate to keep the accuracy loss within $1\%$.
1 code implementation • 9 May 2020 • Tianchen Zhao, Giuseppe Carleo, James Stokes, Shravan Veerapaneni
A notion of quantum natural evolution strategies is introduced, which provides a geometric synthesis of a number of known quantum/classical algorithms for performing classical black-box optimization.
1 code implementation • ECCV 2020 • Xuefei Ning, Tianchen Zhao, Wenshuo Li, Peng Lei, Yu Wang, Huazhong Yang
In budgeted pruning, how to distribute the resources across layers (i. e., sparsity allocation) is the key problem.
1 code implementation • ECCV 2020 • Xuefei Ning, Yin Zheng, Tianchen Zhao, Yu Wang, Huazhong Yang
Experimental results on various search spaces confirm GATES's effectiveness in improving the performance predictor.
no code implementations • ICLR 2019 • Dejiao Zhang, Tianchen Zhao, Laura Balzano
Unlike the Variational Autoencoder framework, IMAE starts from a stochastic encoder that seeks to map each input data to a hybrid discrete and continuous representation with the objective of maximizing the mutual information between the data and their representations.
no code implementations • ICLR 2019 • Tianchen Zhao, Dejiao Zhang, Zeyu Sun, Honglak Lee
We formulate an information-based optimization problem for supervised classification.
no code implementations • ICLR 2019 • Dingdong Yang, Seunghoon Hong, Yunseok Jang, Tianchen Zhao, Honglak Lee
We propose a simple yet highly effective method that addresses the mode-collapse problem in the Conditional Generative Adversarial Network (cGAN).
no code implementations • 21 Mar 2018 • Tianchen Zhao
We interpret part of the experimental results of Shwartz-Ziv and Tishby [2017].