13 citations · 24 across the 5 of their papers we have counts for
5 papers
CLIP Brings Better Features to Visual Aesthetics Learners
Liwu Xu, Jinjin Xu, Yuzhe Yang +3
Image Aesthetics Assessment (IAA) is a challenging task due to its subjective nature and expensive manual annotations. Recent large-scale vision-language models, such as Contrastiv…
A Federated Data-Driven Evolutionary Algorithm for Expensive Multi/Many-objective Optimization
Jinjin Xu, Yaochu Jin, Wenli Du
Data-driven optimization has found many successful applications in the real world and received increased attention in the field of evolutionary optimization. Most existing algorith…
Federated Learning on Non-IID Data: A Survey
Hangyu Zhu, Jinjin Xu, Shiqing Liu +1
Federated learning is an emerging distributed machine learning framework for privacy preservation. However, models trained in federated learning usually have worse performance than…
A Federated Data-Driven Evolutionary Algorithm
Jinjin Xu, Yaochu Jin, Wenli Du +1
Data-driven evolutionary optimization has witnessed great success in solving complex real-world optimization problems. However, existing data-driven optimization algorithms require…
Ternary Compression for Communication-Efficient Federated Learning
Jinjin Xu, Wenli Du, Ran Cheng +2
Learning over massive data stored in different locations is essential in many real-world applications. However, sharing data is full of challenges due to the increasing demands of…