455 citations · 1.3k across the 21 of their papers we have counts for
21 papers
Close Shortcut Wins Long: Seeking Diverse and Stable Generators for Data-Free Knowledge Distillation
Kailin Lyu, Zherui Zhang, Junhao Dong +11
Data-Free Knowledge Distillation (DFKD) preserves privacy by transferring knowledge without real data access. However, existing generator-based DFKD methods suffer from over-relian…
Evidential Domain Adaptation for Remaining Useful Life Prediction with Incomplete Degradation
Yubo Hou, Mohamed Ragab, Yucheng Wang +5
Accurate Remaining Useful Life (RUL) prediction without labeled target domain data is a critical challenge, and domain adaptation (DA) has been widely adopted to address it by tran…
Target-specific Adaptation and Consistent Degradation Alignment for Cross-Domain Remaining Useful Life Prediction
Yubo Hou, Mohamed Ragab, Min Wu +3
Accurate prediction of the Remaining Useful Life (RUL) in machinery can significantly diminish maintenance costs, enhance equipment up-time, and mitigate adverse outcomes. Data-dri…
Generating Reliable Pixel-Level Labels for Source Free Domain Adaptation
Gabriel Tjio, Ping Liu, Yawei Luo +2
This work addresses the challenging domain adaptation setting in which knowledge from the labelled source domain dataset is available only from the pretrained black-box segmentatio…
Dual Stage Stylization Modulation for Domain Generalized Semantic Segmentation
Gabriel Tjio, Ping Liu, Chee-Keong Kwoh +1
Obtaining sufficient labeled data for training deep models is often challenging in real-life applications. To address this issue, we propose a novel solution for single-source doma…
Label-efficient Time Series Representation Learning: A Review
Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen +3
Label-efficient time series representation learning, which aims to learn effective representations with limited labeled data, is crucial for deploying deep learning models in real-…