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Towards Compatible Fine-tuning for Vision-Language Model Updates
Zhengbo Wang, Jian Liang, Lijun Sheng +3
So far, efficient fine-tuning has become a popular strategy for enhancing the capabilities of foundation models on downstream tasks by learning plug-and-play modules. However, exis…
Sample Correlation for Fingerprinting Deep Face Recognition
Jiyang Guan, Jian Liang, Yanbo Wang +1
Face recognition has witnessed remarkable advancements in recent years, thanks to the development of deep learning techniques.However, an off-the-shelf face recognition model as a…
Prototypical Distillation and Debiased Tuning for Black-box Unsupervised Domain Adaptation
Jian Liang, Lijun Sheng, Hongmin Liu +1
Unsupervised domain adaptation aims to transfer knowledge from a related, label-rich source domain to an unlabeled target domain, thereby circumventing the high costs associated wi…
LoRA-Pro: Are Low-Rank Adapters Properly Optimized?
Zhengbo Wang, Jian Liang, Ran He +2
Low-rank adaptation, also known as LoRA, has emerged as a prominent method for parameter-efficient fine-tuning of foundation models. Despite its computational efficiency, LoRA stil…
A Hard-to-Beat Baseline for Training-free CLIP-based Adaptation
Zhengbo Wang, Jian Liang, Lijun Sheng +3
Contrastive Language-Image Pretraining (CLIP) has gained popularity for its remarkable zero-shot capacity. Recent research has focused on developing efficient fine-tuning methods,…
Towards Eliminating Hard Label Constraints in Gradient Inversion Attacks
Yanbo Wang, Jian Liang, Ran He
Gradient inversion attacks aim to reconstruct local training data from intermediate gradients exposed in the federated learning framework. Despite successful attacks, all previous…
Protecting Model Adaptation from Trojans in the Unlabeled Data
Lijun Sheng, Jian Liang, Ran He +2
Model adaptation tackles the distribution shift problem with a pre-trained model instead of raw data, which has become a popular paradigm due to its great privacy protection. Exist…
Exploring Vacant Classes in Label-Skewed Federated Learning
Kuangpu Guo, Yuhe Ding, Jian Liang +3
Label skews, characterized by disparities in local label distribution across clients, pose a significant challenge in federated learning. As minority classes suffer from worse accu…