Author name cluster

Wenhe Liu

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

1 author row

NeurIPS Conference 2024 Conference Paper

Efficient LLM Jailbreak via Adaptive Dense-to-sparse Constrained Optimization

Kai Hu
Weichen Yu
Yining Li
Tianjun Yao
Xiang Li
Wenhe Liu
Lijun Yu
Zhiqiang Shen

Recent research indicates that large language models (LLMs) are susceptible to jailbreaking attacks that can generate harmful content. This paper introduces a novel token-level attack method, Adaptive Dense-to-Sparse Constrained Optimization (ADC), which has been shown to successfully jailbreak multiple open-source LLMs. Drawing inspiration from the difficulties of discrete token optimization, our method relaxes the discrete jailbreak optimization into a continuous optimization process while gradually increasing the sparsity of the optimizing vectors. This technique effectively bridges the gap between discrete and continuous space optimization. Experimental results demonstrate that our method is more effective and efficient than state-of-the-art token-level methods. On Harmbench, our approach achieves the highest attack success rate on seven out of eight LLMs compared to the latest jailbreak methods. \textcolor{red}{Trigger Warning: This paper contains model behavior that can be offensive in nature. }

PDF Details DOI

TIST Journal 2020 Journal Article

Pair-based Uncertainty and Diversity Promoting Early Active Learning for Person Re-identification

Wenhe Liu
Xiaojun Chang
Ling Chen
Dinh Phung
Xiaoqin Zhang
Yi Yang
Alexander G. Hauptmann

The effective training of supervised Person Re-identification (Re-ID) models requires sufficient pairwise labeled data. However, when there is limited annotation resource, it is difficult to collect pairwise labeled data. We consider a challenging and practical problem called Early Active Learning, which is applied to the early stage of experiments when there is no pre-labeled sample available as references for human annotating. Previous early active learning methods suffer from two limitations for Re-ID. First, these instance-based algorithms select instances rather than pairs, which can result in missing optimal pairs for Re-ID. Second, most of these methods only consider the representativeness of instances, which can result in selecting less diverse and less informative pairs. To overcome these limitations, we propose a novel pair-based active learning for Re-ID. Our algorithm selects pairs instead of instances from the entire dataset for annotation. Besides representativeness, we further take into account the uncertainty and the diversity in terms of pairwise relations. Therefore, our algorithm can produce the most representative, informative, and diverse pairs for Re-ID data annotation. Extensive experimental results on five benchmark Re-ID datasets have demonstrated the superiority of the proposed pair-based early active learning algorithm.

Details DOI

TIST Journal 2018 Journal Article

Few-Shot Text and Image Classification via Analogical Transfer Learning

Wenhe Liu
Xiaojun Chang
Yan Yan
Yi Yang
Alexander G. Hauptmann

Learning from very few samples is a challenge for machine learning tasks, such as text and image classification. Performance of such task can be enhanced via transfer of helpful knowledge from related domains, which is referred to as transfer learning. In previous transfer learning works, instance transfer learning algorithms mostly focus on selecting the source domain instances similar to the target domain instances for transfer. However, the selected instances usually do not directly contribute to the learning performance in the target domain. Hypothesis transfer learning algorithms focus on the model/parameter level transfer. They treat the source hypotheses as well-trained and transfer their knowledge in terms of parameters to learn the target hypothesis. Such algorithms directly optimize the target hypothesis by the observable performance improvements. However, they fail to consider the problem that instances that contribute to the source hypotheses may be harmful for the target hypothesis, as instance transfer learning analyzed. To relieve the aforementioned problems, we propose a novel transfer learning algorithm, which follows an analogical strategy. Particularly, the proposed algorithm first learns a revised source hypothesis with only instances contributing to the target hypothesis. Then, the proposed algorithm transfers both the revised source hypothesis and the target hypothesis (only trained with a few samples) to learn an analogical hypothesis. We denote our algorithm as Analogical Transfer Learning. Extensive experiments on one synthetic dataset and three real-world benchmark datasets demonstrate the superior performance of the proposed algorithm.

Details DOI

AAAI Conference 2018 Conference Paper

Semi-Supervised Bayesian Attribute Learning for Person Re-Identification

Wenhe Liu
Xiaojun Chang
Ling Chen
Yi Yang

Person re-identiﬁcation (re-ID) tasks aim to identify the same person in multiple images captured from non-overlapping camera views. Most previous re-ID studies have attempted to solve this problem through either representation learning or metric learning, or by combining both techniques. Representation learning relies on the latent factors or attributes of the data. In most of these works, the dimensionality of the factors/attributes has to be manually determined for each new dataset. Thus, this approach is not robust. Metric learning optimizes a metric across the dataset to measure similarity according to distance. However, choosing the optimal method for computing these distances is data dependent, and learning the appropriate metric relies on a sufﬁcient number of pair-wise labels. To overcome these limitations, we propose a novel algorithm for person re-ID, called semi-supervised Bayesian attribute learning. We introduce an Indian Buffet Process to identify the priors of the latent attributes. The dimensionality of attributes factors is then automatically determined by nonparametric Bayesian learning. Meanwhile, unlike traditional distance metric learning, we propose a reidentiﬁcation probability distribution to describe how likely it is that a pair of images contains the same person. This technique relies solely on the latent attributes of both images. Moreover, pair-wise labels that are not known can be estimated from pair-wise labels that are known, making this a robust approach for semi-supervised learning. Extensive experiments demonstrate the superior performance of our algorithm over several state-of-the-art algorithms on small-scale datasets and comparable performance on large-scale re-ID datasets.

PDF Details