Search Results for author: Jianyang Gu

Found 19 papers, 17 papers with code

Exploring the Impact of Dataset Bias on Dataset Distillation

1 code implementation24 Mar 2024 Yao Lu, Jianyang Gu, Xuguang Chen, Saeed Vahidian, Qi Xuan

Given that there are no suitable biased datasets for DD, we first construct two biased datasets, CMNIST-DD and CCIFAR10-DD, to establish a foundation for subsequent analysis.

Group Distributionally Robust Dataset Distillation with Risk Minimization

1 code implementation7 Feb 2024 Saeed Vahidian, Mingyu Wang, Jianyang Gu, Vyacheslav Kungurtsev, Wei Jiang, Yiran Chen

However, targeting the training dataset must be thought of as auxiliary in the same sense that the training set is an approximate substitute for the population distribution, and the latter is the data of interest.

Federated Learning Neural Architecture Search +1

Efficient Dataset Distillation via Minimax Diffusion

1 code implementation27 Nov 2023 Jianyang Gu, Saeed Vahidian, Vyacheslav Kungurtsev, Haonan Wang, Wei Jiang, Yang You, Yiran Chen

Observing that key factors for constructing an effective surrogate dataset are representativeness and diversity, we design additional minimax criteria in the generative training to enhance these facets for the generated images of diffusion models.

DREAM+: Efficient Dataset Distillation by Bidirectional Representative Matching

1 code implementation23 Oct 2023 Yanqing Liu, Jianyang Gu, Kai Wang, Zheng Zhu, Kaipeng Zhang, Wei Jiang, Yang You

Dataset distillation plays a crucial role in creating compact datasets with similar training performance compared with original large-scale ones.

Transfer Learning

Can pre-trained models assist in dataset distillation?

1 code implementation5 Oct 2023 Yao Lu, Xuguang Chen, Yuchen Zhang, Jianyang Gu, Tianle Zhang, Yifan Zhang, Xiaoniu Yang, Qi Xuan, Kai Wang, Yang You

Dataset Distillation (DD) is a prominent technique that encapsulates knowledge from a large-scale original dataset into a small synthetic dataset for efficient training.

Dataset Quantization

1 code implementation ICCV 2023 Daquan Zhou, Kai Wang, Jianyang Gu, Xiangyu Peng, Dongze Lian, Yifan Zhang, Yang You, Jiashi Feng

Extensive experiments demonstrate that DQ is able to generate condensed small datasets for training unseen network architectures with state-of-the-art compression ratios for lossless model training.

object-detection Object Detection +2

Summarizing Stream Data for Memory-Constrained Online Continual Learning

2 code implementations26 May 2023 Jianyang Gu, Kai Wang, Wei Jiang, Yang You

Through maintaining the consistency of training gradients and relationship to the past tasks, the summarized samples are more representative for the stream data compared to the original images.

Continual Learning Informativeness

MSINet: Twins Contrastive Search of Multi-Scale Interaction for Object ReID

1 code implementation CVPR 2023 Jianyang Gu, Kai Wang, Hao Luo, Chen Chen, Wei Jiang, Yuqiang Fang, Shanghang Zhang, Yang You, Jian Zhao

Neural Architecture Search (NAS) has been increasingly appealing to the society of object Re-Identification (ReID), for that task-specific architectures significantly improve the retrieval performance.

Image Classification Neural Architecture Search +3

DiM: Distilling Dataset into Generative Model

2 code implementations8 Mar 2023 Kai Wang, Jianyang Gu, Daquan Zhou, Zheng Zhu, Wei Jiang, Yang You

To the best of our knowledge, we are the first to achieve higher accuracy on complex architectures than simple ones, such as 75. 1\% with ResNet-18 and 72. 6\% with ConvNet-3 on ten images per class of CIFAR-10.

InfoBatch: Lossless Training Speed Up by Unbiased Dynamic Data Pruning

1 code implementation8 Mar 2023 Ziheng Qin, Kai Wang, Zangwei Zheng, Jianyang Gu, Xiangyu Peng, Zhaopan Xu, Daquan Zhou, Lei Shang, Baigui Sun, Xuansong Xie, Yang You

To solve this problem, we propose \textbf{InfoBatch}, a novel framework aiming to achieve lossless training acceleration by unbiased dynamic data pruning.

Semantic Segmentation

DREAM: Efficient Dataset Distillation by Representative Matching

2 code implementations ICCV 2023 Yanqing Liu, Jianyang Gu, Kai Wang, Zheng Zhu, Wei Jiang, Yang You

Although there are various matching objectives, currently the strategy for selecting original images is limited to naive random sampling.

SoccerNet 2022 Challenges Results

7 code implementations5 Oct 2022 Silvio Giancola, Anthony Cioppa, Adrien Deliège, Floriane Magera, Vladimir Somers, Le Kang, Xin Zhou, Olivier Barnich, Christophe De Vleeschouwer, Alexandre Alahi, Bernard Ghanem, Marc Van Droogenbroeck, Abdulrahman Darwish, Adrien Maglo, Albert Clapés, Andreas Luyts, Andrei Boiarov, Artur Xarles, Astrid Orcesi, Avijit Shah, Baoyu Fan, Bharath Comandur, Chen Chen, Chen Zhang, Chen Zhao, Chengzhi Lin, Cheuk-Yiu Chan, Chun Chuen Hui, Dengjie Li, Fan Yang, Fan Liang, Fang Da, Feng Yan, Fufu Yu, Guanshuo Wang, H. Anthony Chan, He Zhu, Hongwei Kan, Jiaming Chu, Jianming Hu, Jianyang Gu, Jin Chen, João V. B. Soares, Jonas Theiner, Jorge De Corte, José Henrique Brito, Jun Zhang, Junjie Li, Junwei Liang, Leqi Shen, Lin Ma, Lingchi Chen, Miguel Santos Marques, Mike Azatov, Nikita Kasatkin, Ning Wang, Qiong Jia, Quoc Cuong Pham, Ralph Ewerth, Ran Song, RenGang Li, Rikke Gade, Ruben Debien, Runze Zhang, Sangrok Lee, Sergio Escalera, Shan Jiang, Shigeyuki Odashima, Shimin Chen, Shoichi Masui, Shouhong Ding, Sin-wai Chan, Siyu Chen, Tallal El-Shabrawy, Tao He, Thomas B. Moeslund, Wan-Chi Siu, Wei zhang, Wei Li, Xiangwei Wang, Xiao Tan, Xiaochuan Li, Xiaolin Wei, Xiaoqing Ye, Xing Liu, Xinying Wang, Yandong Guo, YaQian Zhao, Yi Yu, YingYing Li, Yue He, Yujie Zhong, Zhenhua Guo, Zhiheng Li

The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team.

Action Spotting Camera Calibration +3

Dynamic Gradient Reactivation for Backward Compatible Person Re-identification

no code implementations12 Jul 2022 Xiao Pan, Hao Luo, Weihua Chen, Fan Wang, Hao Li, Wei Jiang, Jianming Zhang, Jianyang Gu, Peike Li

To address this issue, we propose the Ranking-based Backward Compatible Learning (RBCL), which directly optimizes the ranking metric between new features and old features.

Person Re-Identification Retrieval

An Empirical Study of Vehicle Re-Identification on the AI City Challenge

1 code implementation20 May 2021 Hao Luo, Weihua Chen, Xianzhe Xu, Jianyang Gu, Yuqi Zhang, Chong Liu, Yiqi Jiang, Shuting He, Fan Wang, Hao Li

We mainly focus on four points, i. e. training data, unsupervised domain-adaptive (UDA) training, post-processing, model ensembling in this challenge.

Re-Ranking Retrieval +1

1st Place Solution to VisDA-2020: Bias Elimination for Domain Adaptive Pedestrian Re-identification

1 code implementation25 Dec 2020 Jianyang Gu, Hao Luo, Weihua Chen, Yiqi Jiang, Yuqi Zhang, Shuting He, Fan Wang, Hao Li, Wei Jiang

Considering the large gap between the source domain and target domain, we focused on solving two biases that influenced the performance on domain adaptive pedestrian Re-ID and proposed a two-stage training procedure.

Domain Adaptation Pseudo Label

ZJUNlict Extended Team Description Paper for RoboCup 2019

1 code implementation22 May 2019 Zheyuan Huang, Lingyun Chen, Jiacheng Li, Yunkai Wang, Zexi Chen, Licheng Wen, Jianyang Gu, Peng Hu, Rong Xiong

For the Small Size League of RoboCup 2018, Team ZJUNLict has won the champion and therefore, this paper thoroughly described the devotion which ZJUNLict has devoted and the effort that ZJUNLict has contributed.

Robotics 68T40

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