no code implementations • 6 Jun 2024 • Tian Wang, Chuang Wang
Neural operators effectively solve PDE problems from data without knowing the explicit equations, which learn the map from the input sequences of observed samples to the predicted values.
no code implementations • 26 May 2024 • Haoru Tan, Chuang Wang, Xu-Yao Zhang, Cheng-Lin Liu
The whole algorithm can be considered as a differentiable map from the graph affinity matrix to the prediction of node correspondence.
no code implementations • 22 Mar 2024 • Peng Xu, Haoran Wang, Chuang Wang, Xu Liu
As AI Agents based on Large Language Models (LLMs) have shown potential in practical applications across various fields, how to quickly deploy an AI agent and how to conveniently expand the application scenario of AI agents has become a challenge.
no code implementations • 11 Mar 2024 • Haoru Tan, Chuang Wang, Sitong Wu, Xu-Yao Zhang, Fei Yin, Cheng-Lin Liu
In this paper, we propose a graph neural network (GNN) based approach to combine the advantages of data-driven and traditional methods.
no code implementations • 23 Jan 2024 • Chuang Wang, ZhengPing Li, Yuwen Hao, Lijun Wang, Xiaoxue Li
In order to solve the problems of long training time, large consumption of computing resources and huge parameter amount of GAN network in image generation, this paper proposes an improved GAN network model, which is named Faster Projected GAN, based on Projected GAN.
1 code implementation • 27 Dec 2023 • XiMing Xing, Haitao Zhou, Chuang Wang, Jing Zhang, Dong Xu, Qian Yu
However, existing text-to-SVG generation methods lack editability and struggle with visual quality and result diversity.
1 code implementation • 15 Aug 2023 • XiMing Xing, Chuang Wang, Haitao Zhou, Zhihao Hu, Chongxuan Li, Dong Xu, Qian Yu
In the full-control inversion process, we propose an appearance-energy function to control the color and texture of the final generated photo. Importantly, our Inversion-by-Inversion pipeline is training-free and can accept different types of exemplars for color and texture control.
no code implementations • 4 Aug 2023 • Wenzhuo LIU, Xinjian Wu, Fei Zhu, Mingming Yu, Chuang Wang, Cheng-Lin Liu
This is hard for DNN because it tends to focus on fitting to new classes while ignoring old classes, a phenomenon known as catastrophic forgetting.
1 code implementation • NeurIPS 2023 • XiMing Xing, Chuang Wang, Haitao Zhou, Jing Zhang, Qian Yu, Dong Xu
Even though trained mainly on images, we discover that pretrained diffusion models show impressive power in guiding sketch synthesis.
no code implementations • AAAI 2021 • Haoru Tan, Chuang Wang, Sitong Wu, Tie-Qiang Wang, Xu-Yao Zhang, Cheng-Lin Liu
It consists of three parts: a graph neural network to generate a high-level local feature, an attention-based module to normalize the rotational transform, and a global feature matching module based on proximal optimization.
1 code implementation • CVPR 2021 • Fei Zhu, Xu-Yao Zhang, Chuang Wang, Fei Yin, Cheng-Lin Liu
Despite the impressive performance in many individual tasks, deep neural networks suffer from catastrophic forgetting when learning new tasks incrementally.
no code implementations • 1 Jan 2021 • Fei Zhu, Xu-Yao Zhang, Chuang Wang, Cheng-Lin Liu
In spite of the simplicity, extensive experiments demonstrate that the misclassification detection performance of DNNs can be significantly improved by seeing more generated pseudo-classes during training.
no code implementations • 26 Oct 2020 • Wei Wang, Yimeng Chai, Tao Cui, Chuang Wang, Baohua Zhang, Yue Li, Yi An
In recent studies, Generative Adversarial Network (GAN) is one of the popular schemes to augment the image dataset.
no code implementations • 17 Jun 2020 • Noureldin Hendy, Cooper Sloan, Feng Tian, Pengfei Duan, Nick Charchut, Yuesong Xie, Chuang Wang, James Philbin
Managing the different reference frames and characteristics of the sensors, and merging their observations into a single representation complicates perception.
no code implementations • 31 Jan 2020 • Chuang Wang, Ruimin Hu, Min Hu, Jiang Liu, Ting Ren, Shan He, Ming Jiang, Jing Miao
And we validate our method on the Aff-Wild2 datasets released by the Challenge.
Facial Expression Recognition Facial Expression Recognition (FER)
no code implementations • 5 Nov 2019 • Wei Wang, Chuang Wang, Tao Cui, Yue Li
Especially, Isomorphic WGAN is the best in 15/20 experiments.
no code implementations • NeurIPS 2019 • Chuang Wang, Hong Hu, Yue M. Lu
We present a theoretical analysis of the training process for a single-layer GAN fed by high-dimensional input data.
no code implementations • 17 May 2018 • Chuang Wang, Yonina C. Eldar, Yue M. Lu
In addition to providing asymptotically exact predictions of the dynamic performance of the algorithms, our high-dimensional analysis yields several insights, including an asymptotic equivalence between Oja's method and GROUSE, and a precise scaling relationship linking the amount of missing data to the signal-to-noise ratio.
no code implementations • 8 Dec 2017 • Chuang Wang, Jonathan Mattingly, Yue M. Lu
In addition to characterizing the dynamic performance of online learning algorithms, our asymptotic analysis also provides useful insights.
no code implementations • NeurIPS 2017 • Chuang Wang, Yue M. Lu
As the ambient dimension tends to infinity, and with proper time scaling, we show that the time-varying joint empirical measure of the target feature vector and the estimates provided by the algorithm will converge weakly to a deterministic measured-valued process that can be characterized as the unique solution of a nonlinear PDE.