13 citations · 33 across the 14 of their papers we have counts for
6 papers · 1 filter
Attention Residual Fusion Network with Contrast for Source-free Domain Adaptation
Renrong Shao, Wei Zhang, Jun Wang
Source-free domain adaptation (SFDA) involves training a model on source domain and then applying it to a related target domain without access to the source data and labels during…
Consistent Assistant Domains Transformer for Source-free Domain Adaptation
Renrong Shao, Wei Zhang, Kangyang Luo +2
Source-free domain adaptation (SFDA) aims to address the challenge of adapting to a target domain without accessing the source domain directly. However, due to the inaccessibility…
Data-free Knowledge Distillation for Fine-grained Visual Categorization
Renrong Shao, Wei Zhang, Jianhua Yin +1
Data-free knowledge distillation (DFKD) is a promising approach for addressing issues related to model compression, security privacy, and transmission restrictions. Although the ex…
Source-Free Domain Adaptation for Semantic Segmentation
Yuang Liu, Wei Zhang, Jun Wang
Unsupervised Domain Adaptation (UDA) can tackle the challenge that convolutional neural network(CNN)-based approaches for semantic segmentation heavily rely on the pixel-level anno…
Zero-shot Adversarial Quantization
Yuang Liu, Wei Zhang, Jun Wang
Model quantization is a promising approach to compress deep neural networks and accelerate inference, making it possible to be deployed on mobile and edge devices. To retain the hi…
Learning from a Lightweight Teacher for Efficient Knowledge Distillation
Yuang Liu, Wei Zhang, Jun Wang
Knowledge Distillation (KD) is an effective framework for compressing deep learning models, realized by a student-teacher paradigm requiring small student networks to mimic the sof…