Search Results for author: Dashan Gao

Found 21 papers, 5 papers with code

POP: Prompt Of Prompts for Continual Learning

no code implementations14 Jun 2023 Zhiyuan Hu, Jiancheng Lyu, Dashan Gao, Nuno Vasconcelos

We show that a foundation model equipped with POP learning is able to outperform classic CL methods by a significant margin.

Continual Learning Open-Ended Question Answering

FedPDD: A Privacy-preserving Double Distillation Framework for Cross-silo Federated Recommendation

no code implementations9 May 2023 Sheng Wan, Dashan Gao, Hanlin Gu, Daning Hu

However, in reality, the number of overlapped users is often very small, thus largely limiting the performance of such approaches.

Federated Learning Privacy Preserving

Robust Deep Learning Models Against Semantic-Preserving Adversarial Attack

no code implementations8 Apr 2023 Dashan Gao, Yunce Zhao, Yinghua Yao, Zeqi Zhang, Bifei Mao, Xin Yao

In this paper, we study the robustness of deep learning models against joint perturbations by proposing a novel attack mechanism named Semantic-Preserving Adversarial (SPA) attack, which can then be used to enhance adversarial training.

Adversarial Attack

MobileInst: Video Instance Segmentation on the Mobile

no code implementations30 Mar 2023 Renhong Zhang, Tianheng Cheng, Shusheng Yang, Haoyi Jiang, Shuai Zhang, Jiancheng Lyu, Xin Li, Xiaowen Ying, Dashan Gao, Wenyu Liu, Xinggang Wang

To address those issues, we present MobileInst, a lightweight and mobile-friendly framework for video instance segmentation on mobile devices.

Instance Segmentation Segmentation +2

Dense Network Expansion for Class Incremental Learning

no code implementations CVPR 2023 Zhiyuan Hu, Yunsheng Li, Jiancheng Lyu, Dashan Gao, Nuno Vasconcelos

This is accomplished by the introduction of dense connections between the intermediate layers of the task expert networks, that enable the transfer of knowledge from old to new tasks via feature sharing and reusing.

Class Incremental Learning Incremental Learning

A Survey on Heterogeneous Federated Learning

no code implementations10 Oct 2022 Dashan Gao, Xin Yao, Qiang Yang

Therefore, heterogeneous FL is proposed to address the problem of heterogeneity in FL.

Federated Learning Transfer Learning

Privacy Threats Against Federated Matrix Factorization

no code implementations3 Jul 2020 Dashan Gao, Ben Tan, Ce Ju, Vincent W. Zheng, Qiang Yang

Matrix Factorization has been very successful in practical recommendation applications and e-commerce.

Collaborative Filtering Federated Learning +2

Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention

no code implementations15 Jun 2020 Ce Ju, Ruihui Zhao, Jichao Sun, Xiguang Wei, Bo Zhao, Yang Liu, Hongshan Li, Tianjian Chen, Xinwei Zhang, Dashan Gao, Ben Tan, Han Yu, Chuning He, Yuan Jin

It adopts federated averaging during the model training process, without patient data being taken out of the hospitals during the whole process of model training and forecasting.

Privacy Preserving

Federated Transfer Learning for EEG Signal Classification

1 code implementation26 Apr 2020 Ce Ju, Dashan Gao, Ravikiran Mane, Ben Tan, Yang Liu, Cuntai Guan

The success of deep learning (DL) methods in the Brain-Computer Interfaces (BCI) field for classification of electroencephalographic (EEG) recordings has been restricted by the lack of large datasets.

Classification Domain Adaptation +6

Finding novelty with uncertainty

2 code implementations11 Feb 2020 Jacob C. Reinhold, Yufan He, Shizhong Han, Yunqiang Chen, Dashan Gao, Junghoon Lee, Jerry L. Prince, Aaron Carass

Medical images are often used to detect and characterize pathology and disease; however, automatically identifying and segmenting pathology in medical images is challenging because the appearance of pathology across diseases varies widely.


Validating uncertainty in medical image translation

1 code implementation11 Feb 2020 Jacob C. Reinhold, Yufan He, Shizhong Han, Yunqiang Chen, Dashan Gao, Junghoon Lee, Jerry L. Prince, Aaron Carass

Medical images are increasingly used as input to deep neural networks to produce quantitative values that aid researchers and clinicians.


Outlier Guided Optimization of Abdominal Segmentation

no code implementations10 Feb 2020 Yuchen Xu, Olivia Tang, Yucheng Tang, Ho Hin Lee, Yunqiang Chen, Dashan Gao, Shizhong Han, Riqiang Gao, Michael R. Savona, Richard G. Abramson, Yuankai Huo, Bennett A. Landman

We built on a pre-trained 3D U-Net model for abdominal multi-organ segmentation and augmented the dataset either with outlier data (e. g., exemplars for which the baseline algorithm failed) or inliers (e. g., exemplars for which the baseline algorithm worked).

Active Learning Computed Tomography (CT) +2

Contrast Phase Classification with a Generative Adversarial Network

no code implementations14 Nov 2019 Yucheng Tang, Ho Hin Lee, Yuchen Xu, Olivia Tang, Yunqiang Chen, Dashan Gao, Shizhong Han, Riqiang Gao, Camilo Bermudez, Michael R. Savona, Richard G. Abramson, Yuankai Huo, Bennett A. Landman

Dynamic contrast enhanced computed tomography (CT) is an imaging technique that provides critical information on the relationship of vascular structure and dynamics in the context of underlying anatomy.

Anatomy Classification +3

Semi-Supervised Multi-Organ Segmentation through Quality Assurance Supervision

no code implementations12 Nov 2019 Ho Hin Lee, Yucheng Tang, Olivia Tang, Yuchen Xu, Yunqiang Chen, Dashan Gao, Shizhong Han, Riqiang Gao, Michael R. Savona, Richard G. Abramson, Yuankai Huo, Bennett A. Landman

The contributions of the proposed method are threefold: We show that (1) the QA scores can be used as a loss function to perform semi-supervised learning for unlabeled data, (2) the well trained discriminator is learnt by QA score rather than traditional true/false, and (3) the performance of multi-organ segmentation on unlabeled datasets can be fine-tuned with more robust and higher accuracy than the original baseline method.

Image Segmentation Medical Image Segmentation +4

HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography

1 code implementation11 Sep 2019 Dashan Gao, Ce Ju, Xiguang Wei, Yang Liu, Tianjian Chen, Qiang Yang

To verify the effectiveness of our approach, we conduct experiments on a real-world EEG dataset, consisting of heterogeneous data collected from diverse devices.

EEG Electroencephalogram (EEG) +4

Towards Universal Object Detection by Domain Attention

1 code implementation CVPR 2019 Xudong Wang, Zhaowei Cai, Dashan Gao, Nuno Vasconcelos

Experiments, on a newly established universal object detection benchmark of 11 diverse datasets, show that the proposed detector outperforms a bank of individual detectors, a multi-domain detector, and a baseline universal detector, with a 1. 3x parameter increase over a single-domain baseline detector.

object-detection Object Detection

The discriminant center-surround hypothesis for bottom-up saliency

no code implementations NeurIPS 2007 Dashan Gao, Vijay Mahadevan, Nuno Vasconcelos

The classical hypothesis, that bottom-up saliency is a center-surround process, is combined with a more recent hypothesis that all saliency decisions are optimal in a decision-theoretic sense.

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