Search Results for author: Nanqing Dong

Found 17 papers, 1 papers with code

Region Proposal Network Pre-Training Helps Label-Efficient Object Detection

no code implementations16 Nov 2022 Linus Ericsson, Nanqing Dong, Yongxin Yang, Ales Leonardis, Steven McDonagh

In this work, we propose a simple pretext task that provides an effective pre-training for the RPN, towards efficiently improving downstream object detection performance.

Instance Segmentation object-detection +3

FLock: Defending Malicious Behaviors in Federated Learning with Blockchain

no code implementations5 Nov 2022 Nanqing Dong, Jiahao Sun, Zhipeng Wang, Shuoying Zhang, Shuhao Zheng

Federated learning (FL) is a promising way to allow multiple data owners (clients) to collaboratively train machine learning models without compromising data privacy.

Data Poisoning Federated Learning

Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images

no code implementations30 Jun 2022 Nanqing Dong, Michael Kampffmeyer, Irina Voiculescu

In the second stage, the decentralized partially labeled data are exploited to learn an energy-based multi-label classifier for the common classes.

Federated Learning Object Recognition +2

Revisiting Vicinal Risk Minimization for Partially Supervised Multi-Label Classification Under Data Scarcity

no code implementations19 Apr 2022 Nanqing Dong, Jiayi Wang, Irina Voiculescu

Due to the high human cost of annotation, it is non-trivial to curate a large-scale medical dataset that is fully labeled for all classes of interest.

Multi-Label Classification

Cooperative Multi-Agent Actor-Critic for Privacy-Preserving Load Scheduling in a Residential Microgrid

no code implementations6 Oct 2021 Zhaoming Qin, Nanqing Dong, Eric P. Xing, Junwei Cao

As a scalable data-driven approach, multi-agent reinforcement learning (MARL) has made remarkable advances in solving the cooperative residential load scheduling problems.

Multi-agent Reinforcement Learning Privacy Preserving +2

Residual Contrastive Learning: Unsupervised Representation Learning from Residuals

no code implementations29 Sep 2021 Nanqing Dong, Matteo Maggioni, Yongxin Yang, Eduardo Pérez-Pellitero, Ales Leonardis, Steven McDonagh

In the era of deep learning, supervised residual learning (ResL) has led to many breakthroughs in low-level vision such as image restoration and enhancement tasks.

Contrastive Learning Image Reconstruction +3

Unsupervised Contrastive Learning for Signal-Dependent Noise Synthesis

no code implementations29 Sep 2021 Nanqing Dong, Jianwen Xie, Ping Li

We present a simple yet robust noise synthesis framework based on unsupervised contrastive learning.

Contrastive Learning

Federated Contrastive Learning for Decentralized Unlabeled Medical Images

no code implementations15 Sep 2021 Nanqing Dong, Irina Voiculescu

A label-efficient paradigm in computer vision is based on self-supervised contrastive pre-training on unlabeled data followed by fine-tuning with a small number of labels.

Contrastive Learning Data Augmentation +1

Residual Contrastive Learning for Image Reconstruction: Learning Transferable Representations from Noisy Images

no code implementations18 Jun 2021 Nanqing Dong, Matteo Maggioni, Yongxin Yang, Eduardo Pérez-Pellitero, Ales Leonardis, Steven McDonagh

We propose a new label-efficient learning paradigm based on residuals, residual contrastive learning (RCL), and derive an unsupervised visual representation learning framework, suitable for low-level vision tasks with noisy inputs.

Contrastive Learning Demosaicking +6

Negational Symmetry of Quantum Neural Networks for Binary Pattern Classification

1 code implementation20 May 2021 Nanqing Dong, Michael Kampffmeyer, Irina Voiculescu, Eric Xing

In this work, we provide some theoretical insight into the properties of QNNs by presenting and analyzing a new form of invariance embedded in QNNs for both quantum binary classification and quantum representation learning, which we term negational symmetry.

Classification Representation Learning

Towards Robust Partially Supervised Multi-Structure Medical Image Segmentation on Small-Scale Data

no code implementations28 Nov 2020 Nanqing Dong, Michael Kampffmeyer, Xiaodan Liang, Min Xu, Irina Voiculescu, Eric P. Xing

To bridge the methodological gaps in partially supervised learning (PSL) under data scarcity, we propose Vicinal Labels Under Uncertainty (VLUU), a simple yet efficient framework utilizing the human structure similarity for partially supervised medical image segmentation.

Data Augmentation Image Segmentation +4

Adversarial Domain Adaptation Being Aware of Class Relationships

no code implementations28 May 2019 Zeya Wang, Baoyu Jing, Yang Ni, Nanqing Dong, Pengtao Xie, Eric P. Xing

In this paper, we propose a novel relationship-aware adversarial domain adaptation (RADA) algorithm, which first utilizes a single multi-class domain discriminator to enforce the learning of inter-class dependency structure during domain-adversarial training and then aligns this structure with the inter-class dependencies that are characterized from training the label predictor on source domain.

Domain Adaptation Transfer Learning

Toward Understanding the Impact of Staleness in Distributed Machine Learning

no code implementations ICLR 2019 Wei Dai, Yi Zhou, Nanqing Dong, Hao Zhang, Eric P. Xing

Many distributed machine learning (ML) systems adopt the non-synchronous execution in order to alleviate the network communication bottleneck, resulting in stale parameters that do not reflect the latest updates.

BIG-bench Machine Learning

Reinforced Auto-Zoom Net: Towards Accurate and Fast Breast Cancer Segmentation in Whole-slide Images

no code implementations29 Jul 2018 Nanqing Dong, Michael Kampffmeyer, Xiaodan Liang, Zeya Wang, Wei Dai, Eric P. Xing

Motivated by the zoom-in operation of a pathologist using a digital microscope, RAZN learns a policy network to decide whether zooming is required in a given region of interest.

whole slide images

ConnNet: A Long-Range Relation-Aware Pixel-Connectivity Network for Salient Segmentation

no code implementations20 Apr 2018 Michael Kampffmeyer, Nanqing Dong, Xiaodan Liang, Yu-jia Zhang, Eric P. Xing

We argue that semantic salient segmentation can instead be effectively resolved by reformulating it as a simple yet intuitive pixel-pair based connectivity prediction task.

Semantic Segmentation

SCAN: Structure Correcting Adversarial Network for Organ Segmentation in Chest X-rays

no code implementations26 Mar 2017 Wei Dai, Joseph Doyle, Xiaodan Liang, Hao Zhang, Nanqing Dong, Yuan Li, Eric P. Xing

Through this adversarial process the critic network learns the higher order structures and guides the segmentation model to achieve realistic segmentation outcomes.

Organ Segmentation

Cannot find the paper you are looking for? You can Submit a new open access paper.