Search Results for author: Zhepeng Wang

Found 14 papers, 1 papers with code

Federated Contrastive Learning for Volumetric Medical Image Segmentation

no code implementations23 Apr 2022 Yawen Wu, Dewen Zeng, Zhepeng Wang, Yiyu Shi, Jingtong Hu

However, in medical imaging analysis, each site may only have a limited amount of data and labels, which makes learning ineffective.

Contrastive Learning Federated Learning +3

Federated Contrastive Learning for Dermatological Disease Diagnosis via On-device Learning

no code implementations14 Feb 2022 Yawen Wu, Dewen Zeng, Zhepeng Wang, Yi Sheng, Lei Yang, Alaina J. James, Yiyu Shi, Jingtong Hu

The recently developed self-supervised learning approach, contrastive learning (CL), can leverage the unlabeled data to pre-train a model, after which the model is fine-tuned on limited labeled data for dermatological disease diagnosis.

Contrastive Learning Federated Learning +1

Learn by Challenging Yourself: Contrastive Visual Representation Learning with Hard Sample Generation

no code implementations14 Feb 2022 Yawen Wu, Zhepeng Wang, Dewen Zeng, Yiyu Shi, Jingtong Hu

In this way, the main model learns to cluster hard positives by pulling the representations of similar yet distinct samples together, by which the representations of similar samples are well-clustered and better representations can be learned.

Contrastive Learning Representation Learning +2

Decentralized Unsupervised Learning of Visual Representations

no code implementations21 Nov 2021 Yawen Wu, Zhepeng Wang, Dewen Zeng, Meng Li, Yiyu Shi, Jingtong Hu

To tackle this problem, we propose a collaborative contrastive learning framework consisting of two approaches: feature fusion and neighborhood matching, by which a unified feature space among clients is learned for better data representations.

Contrastive Learning Federated Learning +2

Federated Contrastive Representation Learning with Feature Fusion and Neighborhood Matching

no code implementations29 Sep 2021 Yawen Wu, Zhepeng Wang, Dewen Zeng, Meng Li, Yiyu Shi, Jingtong Hu

Federated learning (FL) enables distributed clients to learn a shared model for prediction while keeping the training data local on each client.

Contrastive Learning Federated Learning +2

Data-Efficient Contrastive Learning by Differentiable Hard Sample and Hard Positive Pair Generation

no code implementations29 Sep 2021 Yawen Wu, Zhepeng Wang, Dewen Zeng, Yiyu Shi, Jingtong Hu

In this way, the main model learns to cluster hard positives by pulling the representations of similar yet distinct samples together, by which the representations of similar samples are well-clustered and better representations can be learned.

Contrastive Learning Self-Supervised Learning

Exploration of Quantum Neural Architecture by Mixing Quantum Neuron Designs

no code implementations8 Sep 2021 Zhepeng Wang, Zhiding Liang, Shanglin Zhou, Caiwen Ding, Yiyu Shi, Weiwen Jiang

Experimental results demonstrate that the identified quantum neural architectures with mixed quantum neurons can achieve 90. 62% of accuracy on the MNIST dataset, compared with 52. 77% and 69. 92% on the VQC and QuantumFlow, respectively.

Can Noise on Qubits Be Learned in Quantum Neural Network? A Case Study on QuantumFlow

no code implementations8 Sep 2021 Zhiding Liang, Zhepeng Wang, Junhuan Yang, Lei Yang, JinJun Xiong, Yiyu Shi, Weiwen Jiang

Specifically, this paper targets quantum neural network (QNN), and proposes to learn the errors in the training phase, so that the identified QNN model can be resilient to noise.

Enabling On-Device Self-Supervised Contrastive Learning With Selective Data Contrast

no code implementations7 Jun 2021 Yawen Wu, Zhepeng Wang, Dewen Zeng, Yiyu Shi, Jingtong Hu

After a model is deployed on edge devices, it is desirable for these devices to learn from unlabeled data to continuously improve accuracy.

Contrastive Learning

Enabling Efficient On-Device Self-supervised Contrastive Learning by Data Selection

no code implementations1 Jan 2021 Yawen Wu, Zhepeng Wang, Dewen Zeng, Yiyu Shi, Jingtong Hu

In this paper, we propose a framework to automatically select the most representative data from unlabeled input stream on-the-fly, which only requires the use of a small data buffer for dynamic learning.

Contrastive Learning

Personalized Deep Learning for Ventricular Arrhythmias Detection on Medical IoT Systems

no code implementations18 Aug 2020 Zhenge Jia, Zhepeng Wang, Feng Hong, Lichuan Ping, Yiyu Shi, Jingtong Hu

We equip the system with real-time inference on both intracardiac and surface rhythm monitors.

Enabling On-Device CNN Training by Self-Supervised Instance Filtering and Error Map Pruning

no code implementations7 Jul 2020 Yawen Wu, Zhepeng Wang, Yiyu Shi, Jingtong Hu

For example, when training ResNet-110 on CIFAR-10, we achieve 68% computation saving while preserving full accuracy and 75% computation saving with a marginal accuracy loss of 1. 3%.

Quantization

Intermittent Inference with Nonuniformly Compressed Multi-Exit Neural Network for Energy Harvesting Powered Devices

no code implementations23 Apr 2020 Yawen Wu, Zhepeng Wang, Zhenge Jia, Yiyu Shi, Jingtong Hu

This work aims to enable persistent, event-driven sensing and decision capabilities for energy-harvesting (EH)-powered devices by deploying lightweight DNNs onto EH-powered devices.

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