1 code implementation • ECCV 2020 • Guangming Wu, Yinqiang Zheng, Zhiling Guo, Zekun Cai, Xiaodan Shi, Xin Ding, Yifei HUANG, Yimin Guo, Ryosuke Shibasaki
In silicon sensors, the interference between visible and near-infrared (NIR) signals is a crucial problem.
1 code implementation • 6 May 2024 • Xin Ding, Yongwei Wang, Kao Zhang, Z. Jane Wang
In this paper, we introduce Continuous Conditional Diffusion Models (CCDMs), the first CDM designed specifically for the CCGM task.
2 code implementations • 17 Mar 2024 • Ruoxuan Yang, Xinyue Zhang, Anais Fernandez-Laaksonen, Xin Ding, Jiangtao Gong
Recently, LLM-powered driver agents have demonstrated considerable potential in the field of autonomous driving, showcasing human-like reasoning and decision-making abilities. However, current research on aligning driver agent behaviors with human driving styles remains limited, partly due to the scarcity of high-quality natural language data from human driving behaviors. To address this research gap, we propose a multi-alignment framework designed to align driver agents with human driving styles through demonstrations and feedback.
no code implementations • 13 Dec 2023 • Xin Ding, Xiaoyu Liu, Zhijun Tu, Yun Zhang, Wei Li, Jie Hu, Hanting Chen, Yehui Tang, Zhiwei Xiong, Baoqun Yin, Yunhe Wang
Post-training quantization (PTQ) has played a key role in compressing large language models (LLMs) with ultra-low costs.
1 code implementation • 20 Aug 2023 • Xin Ding, Yongwei Wang, Zuheng Xu
Although Negative Data Augmentation (NDA) effectively enhances unconditional and class-conditional GANs by introducing anomalies into real training images, guiding the GANs away from low-quality outputs, its impact on CcGANs is limited, as it fails to replicate negative samples that may occur during the CcGAN sampling.
1 code implementation • 13 Jul 2023 • Zhan Shi, Xin Ding, Peng Ding, Chun Yang, Ru Huang, Xiaoxuan Song
Four tiny SOAP models are also created by replacing the convolutional blocks in Mobile-SOAP with four small-scale networks, respectively.
no code implementations • 8 Dec 2022 • Jing Fang, Yinbo Yu, Zhongyuan Wang, Xin Ding, Ruimin Hu
Image super-resolution (SR) is a technique to recover lost high-frequency information in low-resolution (LR) images.
no code implementations • 31 Jul 2021 • Li Ding, Yongwei Wang, Xin Ding, Kaiwen Yuan, Ping Wang, Hua Huang, Z. Jane Wang
Deep learning based image classification models are shown vulnerable to adversarial attacks by injecting deliberately crafted noises to clean images.
2 code implementations • 7 Apr 2021 • Xin Ding, Yongwei Wang, Zuheng Xu, Z. Jane Wang, William J. Welch
Knowledge distillation (KD) has been actively studied for image classification tasks in deep learning, aiming to improve the performance of a student based on the knowledge from a teacher.
1 code implementation • 20 Mar 2021 • Xin Ding, Yongwei Wang, Z. Jane Wang, William J. Welch
When sampling from CcGANs, the superiority of cDR-RS is even more noticeable in terms of both effectiveness and efficiency.
Ranked #1 on Image Generation on RC-49
1 code implementation • ICLR 2021 • Xin Ding, Yongwei Wang, Zuheng Xu, William J. Welch, Z. Jane Wang
This work proposes the continuous conditional generative adversarial network (CcGAN), the first generative model for image generation conditional on continuous, scalar conditions (termed regression labels).
Ranked #2 on Image Generation on RC-49
1 code implementation • 29 Oct 2020 • Yongwei Wang, Xin Ding, Li Ding, Rabab Ward, Z. Jane Wang
Specially, when adversaries consider imperceptibility as a constraint, the proposed anti-forensic method can improve the average attack success rate by around 30\% on fake face images over two baseline attacks.
1 code implementation • 28 Oct 2020 • Xin Ding, Qiong Zhang, William J. Welch
Modern methods often formulate the counting of cells from microscopic images as a regression problem and more or less rely on expensive, manually annotated training images (e. g., dot annotations indicating the centroids of cells or segmentation masks identifying the contours of cells).
no code implementations • 6 Oct 2020 • Chong Chen, Qinghui Xing, Xin Ding, Yaru Xue, Tianfu Zhong
In data assimilation algorithms, the error covariance between the forecasts and observations is used to optimize the parameters.
1 code implementation • 24 Sep 2019 • Xin Ding, Z. Jane Wang, William J. Welch
Our subsampling methods do not rely on the optimality of the discriminator and are suitable for all types of GANs.
1 code implementation • 14 Apr 2016 • Xin Ding, Ziyi Qiu, Xiaohui Chen
Under the sparsity assumption on the transition matrix, we establish the rate of convergence of the proposed estimator and show that the convergence rate depends on the smoothness of the locally stationary VAR processes only through the smoothness of the transition matrix function.
Statistics Theory Applications Statistics Theory
no code implementations • 28 Jan 2016 • Xin Ding, Wei Chen, Ian J. Wassell
In this paper, we propose a joint optimization approach of the sensing matrix and dictionary for a TCS system.