no code implementations • 22 Apr 2024 • Qingyang Wu, Ying Xu, Tingsong Xiao, Yunze Xiao, Yitong Li, Tianyang Wang, Yichi Zhang, Shanghai Zhong, Yuwei Zhang, Wei Lu, Yifan Yang
This study conducts a comprehensive review and analysis of the existing literature on the attitudes of LLMs towards the 17 SDGs, emphasizing the comparison between their attitudes and support for each goal and those of humans.
no code implementations • 16 Mar 2024 • Mingzhou Jiang, Jiaying Zhou, Junde Wu, Tianyang Wang, Yueming Jin, Min Xu
The Segment Anything Model (SAM) gained significant success in natural image segmentation, and many methods have tried to fine-tune it to medical image segmentation.
1 code implementation • 5 Mar 2024 • Zhaoxin Fan, Runmin Jiang, Junhao Wu, Xin Huang, Tianyang Wang, Heng Huang, Min Xu
3D medical image segmentation is a challenging task with crucial implications for disease diagnosis and treatment planning.
no code implementations • 22 Feb 2024 • Panyi Dong, Zhiyu Quan, Brandon Edwards, Shih-han Wang, Runhuan Feng, Tianyang Wang, Patrick Foley, Prashant Shah
In such a way, FL is implemented as a privacy-enhancing collaborative learning technique that addresses the challenges posed by the sensitivity and privacy of data in traditional machine learning solutions.
1 code implementation • 16 Dec 2023 • Wentao Wang, Xuanyao Huang, Tianyang Wang, Swalpa Kumar Roy
This paper explores the image synthesis capabilities of GPT-4, a leading multi-modal large language model.
1 code implementation • bioRxiv 2023 • Yilun Zhang, Wentao Wang, Jiahui Guan, Deepak Kumar Jain, Tianyang Wang, Swalpa Kumar Roy
Drug-target interactions (DTIs) is essential for advancing pharmaceuticals.
no code implementations • 13 Jul 2023 • Zhaoxin Fan, Puquan Pan, Zeren Zhang, Ce Chen, Tianyang Wang, Siyang Zheng, Min Xu
Few-shot medical image semantic segmentation is of paramount importance in the domain of medical image analysis.
no code implementations • 20 Dec 2022 • Siyu Huang, Tianyang Wang, Haoyi Xiong, Bihan Wen, Jun Huan, Dejing Dou
Inspired by the fact that the samples with higher loss are usually more informative to the model than the samples with lower loss, in this paper we present a novel deep active learning approach that queries the oracle for data annotation when the unlabeled sample is believed to incorporate high loss.
no code implementations • 26 May 2022 • Xingjian Li, Pengkun Yang, Yangcheng Gu, Xueying Zhan, Tianyang Wang, Min Xu, Chengzhong Xu
We provide theoretical analyses by leveraging the small Gaussian noise theory and demonstrate that our method favors a subset with large and diverse gradients.
no code implementations • ICCV 2023 • Andong Deng, Xingjian Li, Di Hu, Tianyang Wang, Haoyi Xiong, Chengzhong Xu
Based on the contradictory phenomenon between FE and FT that better feature extractor fails to be fine-tuned better accordingly, we conduct comprehensive analyses on features before softmax layer to provide insightful explanations.
1 code implementation • 10 Dec 2021 • Tianyang Wang, Xingjian Li, Pengkun Yang, Guosheng Hu, Xiangrui Zeng, Siyu Huang, Cheng-Zhong Xu, Min Xu
In this work, we explore such an impact by theoretically proving that selecting unlabeled data of higher gradient norm leads to a lower upper-bound of test loss, resulting in better test performance.
1 code implementation • ICCV 2021 • Siyu Huang, Tianyang Wang, Haoyi Xiong, Jun Huan, Dejing Dou
To lower the cost of data annotation, active learning has been proposed to interactively query an oracle to annotate a small proportion of informative samples in an unlabeled dataset.
1 code implementation • 18 Nov 2020 • Kirill V. Golubnichiy, Tianyang Wang, Andrey V. Nikitin
It was proposed by Klibanov a new empirical mathematical method to work with the Black-Scholes equation.
Numerical Analysis Numerical Analysis 35R30, 65K05, 35R25, 65M30 G.1.8; G.1.6
no code implementations • 7 Oct 2020 • Mihir Rao, Michelle Zhu, Tianyang Wang
In this paper, comprehensive experimental studies of implementing state-of-the-art CNNs for the detection and classification of DR are conducted in order to determine the top performing classifiers for the task.
1 code implementation • 17 Mar 2020 • Siyu Huang, Haoyi Xiong, Tianyang Wang, Bihan Wen, Qingzhong Wang, Zeyu Chen, Jun Huan, Dejing Dou
This paper further presents a real-time feed-forward model to leverage Style Projection for arbitrary image style transfer, which includes a regularization term for matching the semantics between input contents and stylized outputs.
no code implementations • 8 Sep 2018 • Tianyang Wang, Jun Huan, Michelle Zhu
It makes use of pre-trained models that are learned from a source domain, and utilizes these models for the tasks in a target domain.
no code implementations • 1 Sep 2018 • Tianyang Wang, Jun Huan, Bo Li
In this paper, we demonstrate that deep learning models such as convolutional neural networks may not favor all training samples, and generalization accuracy can be further improved by dropping those unfavorable samples.
no code implementations • 27 Aug 2017 • Songqing Yue, Tianyang Wang
To mitigate this issue, we propose a simple yet effective weighted softmax loss which can be employed as the final layer of deep CNNs.
no code implementations • 18 Aug 2017 • Tianyang Wang, Mingxuan Sun, Kaoning Hu
It has been proven that the expansion of receptive field can boost the CNN performance in image classification, and we further demonstrate that it can also lead to competitive performance for denoising problem.
no code implementations • 14 Aug 2017 • Tianyang Wang, Zhengrui Qin, Michelle Zhu
In this paper, we propose a novel convolutional neural network (CNN) for image denoising, which uses exponential linear unit (ELU) as the activation function.