56 citations · 123 across the 22 of their papers we have counts for
7 papers · 1 filter
DLF: Disentangled-Language-Focused Multimodal Sentiment Analysis
Pan Wang, Qiang Zhou, Yawen Wu +2
Multimodal Sentiment Analysis (MSA) leverages heterogeneous modalities, such as language, vision, and audio, to enhance the understanding of human sentiment. While existing models…
EdgeOL: Efficient in-situ Online Learning on Edge Devices
Sheng Li, Geng Yuan, Yue Dai +10
Emerging applications, such as robot-assisted eldercare and object recognition, generally employ deep learning neural networks (DNNs) and naturally require: i) handling streaming-i…
Federated Self-Supervised Contrastive Learning and Masked Autoencoder for Dermatological Disease Diagnosis
Yawen Wu, Dewen Zeng, Zhepeng Wang +5
In dermatological disease diagnosis, the private data collected by mobile dermatology assistants exist on distributed mobile devices of patients. Federated learning (FL) can use de…
The Larger The Fairer? Small Neural Networks Can Achieve Fairness for Edge Devices
Yi Sheng, Junhuan Yang, Yawen Wu +5
Along with the progress of AI democratization, neural networks are being deployed more frequently in edge devices for a wide range of applications. Fairness concerns gradually emer…
Federated Contrastive Learning for Dermatological Disease Diagnosis via On-device Learning
Yawen Wu, Dewen Zeng, Zhepeng Wang +5
Deep learning models have been deployed in an increasing number of edge and mobile devices to provide healthcare. These models rely on training with a tremendous amount of labeled…
Decentralized Unsupervised Learning of Visual Representations
Yawen Wu, Zhepeng Wang, Dewen Zeng +3
Collaborative learning enables distributed clients to learn a shared model for prediction while keeping the training data local on each client. However, existing collaborative lear…
Enabling On-Device Self-Supervised Contrastive Learning With Selective Data Contrast
Yawen Wu, Zhepeng Wang, Dewen Zeng +2
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 has demonstrated it…