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Yuying Wang

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3 papers

2 author rows

EAAI Journal 2025 Journal Article

Enhancing anomaly detection with few-shot fine-tuned long text-to-image models

Jiachen Liu
Jiajia An
Junbin Lu
Zhuoqin Yang
Jinbao Wang
Ping Lu
Yuying Wang
Linlin Shen

Industrial anomaly detection plays a crucial role in the industrial manufacturing field. Currently, utilizing generated data to improve the performance of the anomaly detection model is an effective approach. However, most existing methods often rely on mask-guided synthesis, where the distribution of the generated defects is limited by masks that are typically random or learned by a model. In addition, the scarcity of real anomalous samples makes it difficult for generative models to capture genuine defect patterns and align with the real anomaly distribution. To tackle these issues, we propose DefectGen, the first long-text-guided few-shot text-to-image data generation pipeline for industrial anomaly detection. To improve distribution alignment under limited anomaly samples, DefectGen incorporates a Prompt Generation and Variation Module, which uses MLLMs (Multimodal Large Language Models) to expand few-shot image–text pairs into diverse and semantically rich prompts, and DoKr (Weight-Decomposed Low-Rank Adaptation with Kronecker product), a lightweight fine-tuning strategy with structured low-rank adaptation. To ensure the quality of synthetic data, DefectGen further introduces the Real-Guided Clustering Filter, which selects high-quality generated samples by comparing their features with those of real anomalies. Experiments on the MVTec AD(MVTec AnomalyDetection) dataset show that DefectGen generates more diverse and realistic synthetic anomalies and achieves a 5. 58% average improvement in anomaly classification accuracy compared to state-of-the-art methods. Code and data are available at: https: //anonymous. 4open. science/r/DefectGen-CD04/.

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ICML Conference 2025 Conference Paper

FedSSI: Rehearsal-Free Continual Federated Learning with Synergistic Synaptic Intelligence

Yichen Li 0006
Yuying Wang
Haozhao Wang
Yining Qi
Tianzhe Xiao
Ruixuan Li 0001

Continual Federated Learning (CFL) allows distributed devices to collaboratively learn novel concepts from continuously shifting training data while avoiding knowledge forgetting of previously seen tasks. To tackle this challenge, most current CFL approaches rely on extensive rehearsal of previous data. Despite effectiveness, rehearsal comes at a cost to memory, and it may also violate data privacy. Considering these, we seek to apply regularization techniques to CFL by considering their cost-efficient properties that do not require sample caching or rehearsal. Specifically, we first apply traditional regularization techniques to CFL and observe that existing regularization techniques, especially synaptic intelligence, can achieve promising results under homogeneous data distribution but fail when the data is heterogeneous. Based on this observation, we propose a simple yet effective regularization algorithm for CFL named FedSSI, which tailors the synaptic intelligence for the CFL with heterogeneous data settings. FedSSI can not only reduce computational overhead without rehearsal but also address the data heterogeneity issue. Extensive experiments show that FedSSI achieves superior performance compared to state-of-the-art methods.

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NeurIPS Conference 2025 Conference Paper

Resource-Constrained Federated Continual Learning: What Does Matter?

Yichen Li
Yuying Wang
Jiahua Dong
Haozhao Wang
Yining Qi
Rui Zhang
Ruixuan Li

Federated Continual Learning (FCL) aims to enable sequential privacy-preserving model training on streams of incoming data that vary in edge devices by preserving previous knowledge while adapting to new data. Current FCL literature focuses on restricted data privacy and access to previously seen data while imposing no constraints on the training overhead. This is unreasonable for FCL applications in real-world scenarios, where edge devices are primarily constrained by resources such as storage, computational budget, and label rate. We revisit this problem with a large-scale benchmark and analyze the performance of state-of-the-art FCL approaches under different resource-constrained settings. Various typical FCL techniques and six datasets in two incremental learning scenarios (Class-IL and Domain-IL) are involved in our experiments. Through extensive experiments amounting to a total of over 1, 000+ GPU hours, we find that, under limited resource-constrained settings, existing FCL approaches, with no exception, fail to achieve the expected performance. Our conclusions are consistent in the sensitivity analysis. This suggests that most existing FCL methods are particularly too resource-dependent for real-world deployment. Moreover, we study the performance of typical FCL techniques with resource constraints and shed light on future research directions in FCL.

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