Search Results for author: Yuan Du

Found 12 papers, 2 papers with code

Decomposing the Neurons: Activation Sparsity via Mixture of Experts for Continual Test Time Adaptation

1 code implementation26 May 2024 Rongyu Zhang, Aosong Cheng, Yulin Luo, Gaole Dai, Huanrui Yang, Jiaming Liu, ran Xu, Li Du, Yuan Du, Yanbing Jiang, Shanghang Zhang

Continual Test-Time Adaptation (CTTA), which aims to adapt the pre-trained model to ever-evolving target domains, emerges as an important task for vision models.

feature selection Test-time Adaptation

Intuition-aware Mixture-of-Rank-1-Experts for Parameter Efficient Finetuning

no code implementations13 Apr 2024 Yijiang Liu, Rongyu Zhang, Huanrui Yang, Kurt Keutzer, Yuan Du, Li Du, Shanghang Zhang

Large Language Models (LLMs) have demonstrated significant potential in performing multiple tasks in multimedia applications, ranging from content generation to interactive entertainment, and artistic creation.

Unimodal Training-Multimodal Prediction: Cross-modal Federated Learning with Hierarchical Aggregation

no code implementations27 Mar 2023 Rongyu Zhang, Xiaowei Chi, Guiliang Liu, Wenyi Zhang, Yuan Du, Fangxin Wang

Multimodal learning has seen great success mining data features from multiple modalities with remarkable model performance improvement.

Decoder Federated Learning +1

EBM Life Cycle: MCMC Strategies for Synthesis, Defense, and Density Modeling

1 code implementation24 May 2022 Mitch Hill, Jonathan Mitchell, Chu Chen, Yuan Du, Mubarak Shah, Song-Chun Zhu

This work presents strategies to learn an Energy-Based Model (EBM) according to the desired length of its MCMC sampling trajectories.

Adversarial Defense Image Generation +1

Memory-Efficient CNN Accelerator Based on Interlayer Feature Map Compression

no code implementations12 Oct 2021 Zhuang Shao, Xiaoliang Chen, Li Du, Lei Chen, Yuan Du, Wei Zhuang, Huadong Wei, Chenjia Xie, Zhongfeng Wang

To maintain real-time processing in embedded systems, large on-chip memory is required to buffer the interlayer feature maps.

Feature Compression Quantization

A Streaming Accelerator for Deep Convolutional Neural Networks with Image and Feature Decomposition for Resource-limited System Applications

no code implementations15 Sep 2017 Yuan Du, Li Du, Yilei Li, Junjie Su, Mau-Chung Frank Chang

Deep convolutional neural networks (CNN) are widely used in modern artificial intelligence (AI) and smart vision systems but also limited by computation latency, throughput, and energy efficiency on a resource-limited scenario, such as mobile devices, internet of things (IoT), unmanned aerial vehicles (UAV), and so on.

A Reconfigurable Streaming Deep Convolutional Neural Network Accelerator for Internet of Things

no code implementations8 Jul 2017 Li Du, Yuan Du, Yilei Li, Mau-Chung Frank Chang

To implement image detection using CNN in the internet of things (IoT) devices, a streaming hardware accelerator is proposed.

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