6 citations · 16 across the 15 of their papers we have counts for
6 papers · 1 filter
Beyond Pixels: Exploring Human-Readable SVG Generation for Simple Images with Vision Language Models
Tong Zhang, Haoyang Liu, Peiyan Zhang +2
In the field of computer graphics, the use of vector graphics, particularly Scalable Vector Graphics (SVG), represents a notable development from traditional pixel-based imagery. S…
Dataset Distillation via the Wasserstein Metric
Haoyang Liu, Yijiang Li, Tiancheng Xing +5
Dataset Distillation (DD) aims to generate a compact synthetic dataset that enables models to achieve performance comparable to training on the full large dataset, significantly re…
Choosing Wisely and Learning Deeply: Selective Cross-Modality Distillation via CLIP for Domain Generalization
Jixuan Leng, Yijiang Li, Haohan Wang
Domain Generalization (DG), a crucial research area, seeks to train models across multiple domains and test them on unseen ones. In this paper, we introduce a novel approach, namel…
Towards Understanding Adversarial Transferability in Federated Learning
Yijiang Li, Ying Gao, Haohan Wang
We investigate a specific security risk in FL: a group of malicious clients has impacted the model during training by disguising their identities and acting as benign clients but l…
Foundation Model-oriented Robustness: Robust Image Model Evaluation with Pretrained Models
Peiyan Zhang, Haoyang Liu, Chaozhuo Li +3
Machine learning has demonstrated remarkable performance over finite datasets, yet whether the scores over the fixed benchmarks can sufficiently indicate the model's performance in…
Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectives
Haoyang Liu, Maheep Chaudhary, Haohan Wang
The trustworthiness of machine learning has emerged as a critical topic in the field, encompassing various applications and research areas such as robustness, security, interpretab…