Search Results for author: Jincheng Yu

Found 7 papers, 2 papers with code

Mixup Augmentation with Multiple Interpolations

no code implementations3 Jun 2024 Lifeng Shen, Jincheng Yu, Hansi Yang, James T. Kwok

Mixup and its variants form a popular class of data augmentation techniques. Using a random sample pair, it generates a new sample by linear interpolation of the inputs and labels.

Data Augmentation

PixArt-$α$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis

3 code implementations30 Sep 2023 Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao, Enze Xie, Yue Wu, Zhongdao Wang, James Kwok, Ping Luo, Huchuan Lu, Zhenguo Li

We hope PIXART-$\alpha$ will provide new insights to the AIGC community and startups to accelerate building their own high-quality yet low-cost generative models from scratch.

Image Generation Language Modelling

MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models

1 code implementation21 Sep 2023 Longhui Yu, Weisen Jiang, Han Shi, Jincheng Yu, Zhengying Liu, Yu Zhang, James T. Kwok, Zhenguo Li, Adrian Weller, Weiyang Liu

Our MetaMath-7B model achieves 66. 4% on GSM8K and 19. 4% on MATH, exceeding the state-of-the-art models of the same size by 11. 5% and 8. 7%.

Ranked #57 on Arithmetic Reasoning on GSM8K (using extra training data)

Arithmetic Reasoning GSM8K +4

DiffFlow: A Unified SDE Framework for Score-Based Diffusion Models and Generative Adversarial Networks

no code implementations5 Jul 2023 Jingwei Zhang, Han Shi, Jincheng Yu, Enze Xie, Zhenguo Li

Generative models can be categorized into two types: explicit generative models that define explicit density forms and allow exact likelihood inference, such as score-based diffusion models (SDMs) and normalizing flows; implicit generative models that directly learn a transformation from the prior to the data distribution, such as generative adversarial nets (GANs).

Denoising

Attentional Separation-and-Aggregation Network for Self-supervised Depth-Pose Learning in Dynamic Scenes

no code implementations18 Nov 2020 Feng Gao, Jincheng Yu, Hao Shen, Yu Wang, Huazhong Yang

Learning depth and ego-motion from unlabeled videos via self-supervision from epipolar projection can improve the robustness and accuracy of the 3D perception and localization of vision-based robots.

Hu-Fu: Hardware and Software Collaborative Attack Framework against Neural Networks

no code implementations14 May 2018 Wenshuo Li, Jincheng Yu, Xuefei Ning, Pengjun Wang, Qi Wei, Yu Wang, Huazhong Yang

So, in this paper, we propose a hardware-software collaborative attack framework to inject hidden neural network Trojans, which works as a back-door without requiring manipulating input images and is flexible for different scenarios.

Autonomous Driving Cloud Computing +6

A Survey of FPGA Based Neural Network Accelerator

no code implementations24 Dec 2017 Kaiyuan Guo, Shulin Zeng, Jincheng Yu, Yu Wang, Huazhong Yang

Various FPGA based accelerator designs have been proposed with software and hardware optimization techniques to achieve high speed and energy efficiency.

Hardware Architecture

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