Search Results for author: Guang Li

Found 24 papers, 6 papers with code

Importance-Aware Adaptive Dataset Distillation

no code implementations29 Jan 2024 Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama

Based on this observation, we propose an importance-aware adaptive dataset distillation (IADD) method that can improve distillation performance by automatically assigning importance weights to different network parameters during distillation, thereby synthesizing more robust distilled datasets.

PainSeeker: An Automated Method for Assessing Pain in Rats Through Facial Expressions

no code implementations6 Nov 2023 Liu Liu, Guang Li, Dingfan Deng, Jinhua Yu, Yuan Zong

In this letter, we aim to investigate whether laboratory rats' pain can be automatically assessed through their facial expressions.

Boosting Automatic COVID-19 Detection Performance with Self-Supervised Learning and Batch Knowledge Ensembling

no code implementations19 Dec 2022 Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama

On the other hand, batch knowledge ensembling-based fine-tuning can utilize category knowledge of images in a batch according to their visual feature similarities to improve detection performance.

Self-Supervised Learning Transfer Learning

COVID-19 Detection Based on Self-Supervised Transfer Learning Using Chest X-Ray Images

no code implementations19 Dec 2022 Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama

We compared six self-supervised learning (SSL) methods (Cross, BYOL, SimSiam, SimCLR, PIRL-jigsaw, and PIRL-rotation) with the proposed method.

Representation Learning Self-Supervised Learning +1

RGMIM: Region-Guided Masked Image Modeling for Learning Meaningful Representation from X-Ray Images

no code implementations1 Nov 2022 Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama

To address this issue, this work aims to improve MIM for medical images and evaluate its effectiveness in an open X-ray image dataset.

Self-Supervised Learning valid

Dataset Complexity Assessment Based on Cumulative Maximum Scaled Area Under Laplacian Spectrum

no code implementations29 Sep 2022 Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama

Dataset complexity assessment aims to predict classification performance on a dataset with complexity calculation before training a classifier, which can also be used for classifier selection and dataset reduction.


Compressed Gastric Image Generation Based on Soft-Label Dataset Distillation for Medical Data Sharing

1 code implementation29 Sep 2022 Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama

Furthermore, our method can extract essential weights of DCNN models to reduce the memory required to save trained models for efficient medical data sharing.

Image Generation valid

Dataset Distillation Using Parameter Pruning

1 code implementation29 Sep 2022 Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama

In this study, we propose a novel dataset distillation method based on parameter pruning.

Dataset Distillation for Medical Dataset Sharing

1 code implementation29 Sep 2022 Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama

Sharing medical datasets between hospitals is challenging because of the privacy-protection problem and the massive cost of transmitting and storing many high-resolution medical images.

TriBYOL: Triplet BYOL for Self-Supervised Representation Learning

no code implementations7 Jun 2022 Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama

Our method provides a feasible solution for self-supervised learning with real-world high-resolution images that uses small batch sizes.

Representation Learning Self-Supervised Learning

Unsupervised cross-user adaptation in taste sensation recognition based on surface electromyography with conformal prediction and domain regularized component analysis

no code implementations20 Oct 2021 Hengyang Wang, Xianghao Zhan, Li Liu, Asif Ullah, Huiyan Li, Han Gao, You Wang, Guang Li

The results show that DRCA improved the classification accuracy on six subjects (p < 0. 05), compared with the baseline models trained only with the source domain data;, while CPSC did not guarantee the accuracy improvement.

Conformal Prediction Data Augmentation

Classifying herbal medicine origins by temporal and spectral data mining of electronic nose

1 code implementation14 Apr 2021 Li Liu, Xianghao Zhan, Ziheng Duan, Yi Wu, Rumeng Wu, Xiaoqing Guan, Zhan Wang, You Wang, Guang Li

In this study, we classified different origins of three categories of herbal medicines with different feature extraction methods: manual feature extraction, mathematical transformation, deep learning algorithms.

Dimensionality Reduction

Self-Supervised Learning for Gastritis Detection with Gastric X-ray Images

no code implementations7 Apr 2021 Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama

The effectiveness of the proposed self-supervised learning method in gastritis detection is verified using a few annotated gastric X-ray images.

Self-Supervised Learning Specificity

Soft-Label Anonymous Gastric X-ray Image Distillation

1 code implementation7 Apr 2021 Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama

The idea of our distillation method is to extract the valid information of the medical dataset and generate a tiny distilled dataset that has a different data distribution.


A review of smartphones based indoor positioning: challenges and applications

no code implementations3 Jun 2020 Khuong An Nguyen, Zhiyuan Luo, Guang Li, Chris Watkins

The continual proliferation of mobile devices has encouraged much effort in using the smartphones for indoor positioning.

Tool Breakage Detection using Deep Learning

no code implementations16 Aug 2018 Guang Li, Xin Yang, DuanBing Chen, Anxing Song, Yuke Fang, Junlin Zhou

In this work, we use the spindle current approach to detect the breakage of machine tools, which has the high performance of real-time monitoring, low cost, and easy to install.


CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE)

no code implementations10 Aug 2018 Chenyu You, Guang Li, Yi Zhang, Xiaoliu Zhang, Hongming Shan, Shenghong Ju, Zhen Zhao, Zhuiyang Zhang, Wenxiang Cong, Michael W. Vannier, Punam K. Saha, Ge Wang

Specifically, with the generative adversarial network (GAN) as the building block, we enforce the cycle-consistency in terms of the Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs.

Computed Tomography (CT) Generative Adversarial Network +2

Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising

no code implementations2 May 2018 Chenyu You, Qingsong Yang, Hongming Shan, Lars Gjesteby, Guang Li, Shenghong Ju, Zhuiyang Zhang, Zhen Zhao, Yi Zhang, Wenxiang Cong, Ge Wang

However, the radiation dose reduction compromises the signal-to-noise ratio (SNR), leading to strong noise and artifacts that down-grade CT image quality.

Computed Tomography (CT) Denoising

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