Jiangang Lu

ICML Conference 2024 Conference Paper

Rethinking Guidance Information to Utilize Unlabeled Samples: A Label Encoding Perspective

• Yulong Zhang 0005
• Yuan Yao 0016
• Shuhao Chen
• Pengrong Jin
• Yu Zhang 0006
• Jian Jin
• Jiangang Lu

Empirical Risk Minimization (ERM) is fragile in scenarios with insufficient labeled samples. A vanilla extension of ERM to unlabeled samples is Entropy Minimization (EntMin), which employs the soft-labels of unlabeled samples to guide their learning. However, EntMin emphasizes prediction discriminability while neglecting prediction diversity. To alleviate this issue, in this paper, we rethink the guidance information to utilize unlabeled samples. By analyzing the learning objective of ERM, we find that the guidance information for labeled samples in a specific category is the corresponding label encoding. Inspired by this finding, we propose a Label-Encoding Risk Minimization (LERM). It first estimates the label encodings through prediction means of unlabeled samples and then aligns them with their corresponding ground-truth label encodings. As a result, the LERM ensures both prediction discriminability and diversity, and it can be integrated into existing methods as a plugin. Theoretically, we analyze the relationships between LERM and ERM as well as EntMin. Empirically, we verify the superiority of the LERM under several label insufficient scenarios. The codes are available at https: //github. com/zhangyl660/LERM.

NeurIPS Conference 2024 Conference Paper

Time-Varying LoRA: Towards Effective Cross-Domain Fine-Tuning of Diffusion Models

• Zhan Zhuang
• Yulong Zhang
• Xuehao Wang
• Jiangang Lu
• Ying Wei
• Yu Zhang

Large-scale diffusion models are adept at generating high-fidelity images and facilitating image editing and interpolation. However, they have limitations when tasked with generating images in dynamic, evolving domains. In this paper, we introduce Terra, a novel Time-varying low-rank adapter that offers a fine-tuning framework specifically tailored for domain flow generation. The key innovation of Terra lies in its construction of a continuous parameter manifold through a time variable, with its expressive power analyzed theoretically. This framework not only enables interpolation of image content and style but also offers a generation-based approach to address the domain shift problems in unsupervised domain adaptation and domain generalization. Specifically, Terra transforms images from the source domain to the target domain and generates interpolated domains with various styles to bridge the gap between domains and enhance the model generalization, respectively. We conduct extensive experiments on various benchmark datasets, empirically demonstrate the effectiveness of Terra. Our source code is publicly available on https: //github. com/zwebzone/terra.

EAAI Journal 2023 Journal Article

Domain adaptation via Transferable Swin Transformer for tire defect detection

• Yulong Zhang
• Yilin Wang
• Zhiqiang Jiang
• Li Zheng
• Jinshui Chen
• Jiangang Lu

AAAI Conference 2022 Conference Paper

Blindfolded Attackers Still Threatening: Strict Black-Box Adversarial Attacks on Graphs

• Jiarong Xu
• Yizhou Sun
• Xin Jiang
• Yanhao Wang
• Chunping Wang
• Jiangang Lu
• Yang Yang

Adversarial attacks on graphs have attracted considerable research interests. Existing works assume the attacker is either (partly) aware of the victim model, or able to send queries to it. These assumptions are, however, unrealistic. To bridge the gap between theoretical graph attacks and real-world scenarios, in this work, we propose a novel and more realistic setting: strict black-box graph attack, in which the attacker has no knowledge about the victim model at all and is not allowed to send any queries. To design such an attack strategy, we first propose a generic graph filter to unify different families of graph-based models. The strength of attacks can then be quantified by the change in the graph filter before and after attack. By maximizing this change, we are able to find an effective attack strategy, regardless of the underlying model. To solve this optimization problem, we also propose a relaxation technique and approximation theories to reduce the difficulty as well as the computational expense. Experiments demonstrate that, even with no exposure to the model, the Macro-F1 drops 5. 5% in node classification and 29. 5% in graph classification, which is a significant result compared with existent works.

AAAI Conference 2022 Conference Paper

Unsupervised Adversarially Robust Representation Learning on Graphs

• Jiarong Xu
• Yang Yang
• Junru Chen
• Xin Jiang
• Chunping Wang
• Jiangang Lu
• Yizhou Sun

Unsupervised/self-supervised pre-training methods for graph representation learning have recently attracted increasing research interests, and they can be generalized to various downstream applications. Yet, the adversarial robustness of such pre-trained graph learning models remains largely unexplored. More importantly, most existing defense techniques for endto-end graph representation learning methods require prespecified label definitions, and thus cannot be directly applied to the pre-training methods. In this paper, we propose an unsupervised defense technique to robustify pre-trained deep graph models, so that the perturbations on the input graph can be successfully identified and blocked before the model is applied to different downstream tasks. Specifically, we introduce a mutual information-based measure, graph representation vulnerability (GRV), to quantify the robustness of graph encoders on the representation space. We then formulate an optimization problem to learn the graph representation by carefully balancing the trade-off between the expressive power and the robustness (i. e. , GRV) of the graph encoder. The discrete nature of graph topology and the joint space of graph data make the optimization problem intractable to solve. To handle the above difficulty and to reduce computational expense, we further relax the problem and thus provide an approximate solution. Additionally, we explore a provable connection between the robustness of the unsupervised graph encoder and that of models on downstream tasks. Extensive experiments demonstrate that even without access to labels and tasks, our model is still able to enhance robustness against adversarial attacks on three downstream tasks (i. e. , node classification, link prediction, and community detection) by an average of +16. 5% compared with existing methods.