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Meng Xiao

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7 papers

1 author row

NeurIPS Conference 2024 Conference Paper

Distribution-Aware Data Expansion with Diffusion Models

Haowei Zhu
Ling Yang
Jun-Hai Yong
Hongzhi Yin
Jiawei Jiang
Meng Xiao
Wentao Zhang
Bin Wang

The scale and quality of a dataset significantly impact the performance of deep models. However, acquiring large-scale annotated datasets is both a costly and time-consuming endeavor. To address this challenge, dataset expansion technologies aim to automatically augment datasets, unlocking the full potential of deep models. Current data expansion techniques include image transformation and image synthesis methods. Transformation-based methods introduce only local variations, leading to limited diversity. In contrast, synthesis-based methods generate entirely new content, greatly enhancing informativeness. However, existing synthesis methods carry the risk of distribution deviations, potentially degrading model performance with out-of-distribution samples. In this paper, we propose DistDiff, a training-free data expansion framework based on the distribution-aware diffusion model. DistDiff constructs hierarchical prototypes to approximate the real data distribution, optimizing latent data points within diffusion models with hierarchical energy guidance. We demonstrate its capability to generate distribution-consistent samples, significantly improving data expansion tasks. DistDiff consistently enhances accuracy across a diverse range of datasets compared to models trained solely on original data. Furthermore, our approach consistently outperforms existing synthesis-based techniques and demonstrates compatibility with widely adopted transformation-based augmentation methods. Additionally, the expanded dataset exhibits robustness across various architectural frameworks.

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IJCAI Conference 2024 Conference Paper

FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimization

Zhiyuan Ning
Chunlin Tian
Meng Xiao
Wei Fan
Pengyang Wang
Li Li
Pengfei Wang
Yuanchun Zhou

Federated Learning faces significant challenges in statistical and system heterogeneity, along with high energy consumption, necessitating efficient client selection strategies. Traditional approaches, including heuristic and learning-based methods, fall short of addressing these complexities holistically. In response, we propose FedGCS, a novel generative client selection framework that innovatively recasts the client selection process as a generative task. Drawing inspiration from the methodologies used in large language models, FedGCS efficiently encodes abundant decision-making knowledge within a continuous representation space, enabling efficient gradient-based optimization to search for optimal client selection that will be finally output via generation. The framework comprises four steps: (1) automatic collection of diverse “selection-score” pair data using classical client selection methods; (2) training an encoder-evaluator-decoder framework on this data to construct a continuous representation space; (3) employing gradient-based optimization in this space for optimal client selection; (4) generating the final optimal client selection via using beam search for the well-trained decoder. FedGCS outperforms traditional methods by being more comprehensive, generalizable, and efficient, simultaneously optimizing for model performance, latency, and energy consumption. The effectiveness of FedGCS is proven through extensive experimental analyses.

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AIJ Journal 2024 Journal Article

Temporal inductive path neural network for temporal knowledge graph reasoning

Hao Dong
Pengyang Wang
Meng Xiao
Zhiyuan Ning
Pengfei Wang
Yuanchun Zhou

Temporal Knowledge Graph (TKG) is an extension of traditional Knowledge Graph (KG) that incorporates the dimension of time. Reasoning on TKGs is a crucial task that aims to predict future facts based on historical occurrences. The key challenge lies in uncovering structural dependencies within historical subgraphs and temporal patterns. Most existing approaches model TKGs relying on entity modeling, as nodes in the graph play a crucial role in knowledge representation. However, the real-world scenario often involves an extensive number of entities, with new entities emerging over time. This makes it challenging for entity-dependent methods to cope with extensive volumes of entities, and effectively handling newly emerging entities also becomes a significant challenge. Therefore, we propose Temporal Inductive Path Neural Network (TiPNN), which models historical information in an entity-independent perspective. Specifically, TiPNN adopts a unified graph, namely history temporal graph, to comprehensively capture and encapsulate information from history. Subsequently, we utilize the defined query-aware temporal paths on a history temporal graph to model historical path information related to queries for reasoning. Extensive experiments illustrate that the proposed model not only attains significant performance enhancements but also handles inductive settings, while additionally facilitating the provision of reasoning evidence through history temporal graphs.

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EAAI Journal 2023 Journal Article

Fine coordinate attention for surface defect detection

Meng Xiao
Bo Yang
Shilong Wang
Zhengping Zhang
Yan He

Surface defect detection remains a challenging task due to issues such as inconspicuous targets, significant variations among identical defects, and minimal differences between distinct defects. To address these challenges, a Fine Coordinate Attention (FCA) block is proposed in this paper, which encodes both average and salient information in two coordinate directions, so that the spatial dependence can be captured and the long-range interaction can be achieved. And such localization-friendly information is crucial for industrial surface defect images with subtle targets. Specifically, the FCA block can recalibrate feature maps of a surface defect image through three steps: coordinate information aggregation, cross-dimension interaction, and attention generation. It can be embedded into any convolutional neural network (CNN) structure to improve performance. Additionally, two resistance spot welding (RSW) surface defect datasets are published in this paper: an image classification dataset RSW-C and an object detection dataset RSW-D. Experimental results for image classification and object detection demonstrate that the FCA block outperforms existing attention mechanisms. The code is available at, while the two RSW datasets can be found at www. kaggle. com/datasets/alfredzimmer/rswdatasets.

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NeurIPS Conference 2023 Conference Paper

Reinforcement-Enhanced Autoregressive Feature Transformation: Gradient-steered Search in Continuous Space for Postfix Expressions

Dongjie Wang
Meng Xiao
Min Wu
Pengfei Wang
Yuanchun Zhou
Yanjie Fu

Feature transformation aims to generate new pattern-discriminative feature space from original features to improve downstream machine learning (ML) task performances. However, the discrete search space for the optimal feature explosively grows on the basis of combinations of features and operations from low-order forms to high-order forms. Existing methods, such as exhaustive search, expansion reduction, evolutionary algorithms, reinforcement learning, and iterative greedy, suffer from large search space. Overly emphasizing efficiency in algorithm design usually sacrifice stability or robustness. To fundamentally fill this gap, we reformulate discrete feature transformation as a continuous space optimization task and develop an embedding-optimization-reconstruction framework. This framework includes four steps: 1) reinforcement-enhanced data preparation, aiming to prepare high-quality transformation-accuracy training data; 2) feature transformation operation sequence embedding, intending to encapsulate the knowledge of prepared training data within a continuous space; 3) gradient-steered optimal embedding search, dedicating to uncover potentially superior embeddings within the learned space; 4) transformation operation sequence reconstruction, striving to reproduce the feature transformation solution to pinpoint the optimal feature space. Finally, extensive experiments and case studies are performed to demonstrate the effectiveness and robustness of the proposed method. The code and data are publicly accessible https: //www. dropbox. com/sh/imh8ckui7va3k5u/AACulQegVx0MuywYyoCqSdVPa? dl=0.

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IJCAI Conference 2023 Conference Paper

Semi-supervised Domain Adaptation in Graph Transfer Learning

Ziyue Qiao
Xiao Luo
Meng Xiao
Hao Dong
Yuanchun Zhou
Hui Xiong

As a specific case of graph transfer learning, unsupervised domain adaptation on graphs aims for knowledge transfer from label-rich source graphs to unlabeled target graphs. However, graphs with topology and attributes usually have considerable cross-domain disparity and there are numerous real-world scenarios where merely a subset of nodes are labeled in the source graph. This imposes critical challenges on graph transfer learning due to serious domain shifts and label scarcity. To address these challenges, we propose a method named Semi-supervised Graph Domain Adaptation (SGDA). To deal with the domain shift, we add adaptive shift parameters to each of the source nodes, which are trained in an adversarial manner to align the cross-domain distributions of node embedding. Thus, the node classifier trained on labeled source nodes can be transferred to the target nodes. Moreover, to address the label scarcity, we propose pseudo-labeling on unlabeled nodes, which improves classification on the target graph via measuring the posterior influence of nodes based on their relative position to the class centroids. Finally, extensive experiments on a range of publicly accessible datasets validate the effectiveness of our proposed SGDA in different experimental settings.

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EAAI Journal 2022 Journal Article

A global interactive attention-based lightweight denoising network for locating internal defects of CFRP laminates

Bo Yang
Yang Zhang
Shilong Wang
Weichun Xu
Meng Xiao
Yan He
Fan Mo

Carbon fiber reinforced plastic (CFRP) has become one of the main structural materials for aerospace vehicles. However, some internal defects are prone to occur and have potential to cause significant losses of life and property. Currently, the detection of internal defects for CFRP mainly relies on ultrasonic, and other technologies, while they have disadvantages of low efficiency, and poor adaptability. Therefore, this paper explores a novel method to locate internal defects of CFRP laminates by analyzing vibration signals. Firstly, a signal acquisition scheme is designed. Then, a global interactive attention-based lightweight denoising network (GIALDN) is designed to analyze vibration signals and locate internal defects of CFRP laminates. In GIALDN, the threshold denoising method is used to eliminate noise-related features and improve feature discrimination; a global interactive attention module is designed, which makes the network pay more attention to the valid features while realizing the global interactive connection and obtains the rich contextual features; combining with the convolution layer of de-pooling strategy and multi-layer convolution using the residual connection, the backbone of the network is formed. Finally, an experimental platform is established to test the performance of GIALDN. Results show that the location accuracy of GIALDN can reach 98. 68%, which is more than 15% higher than those of VGGnet11 and FaultNet, and is also superior to those of LSTM, RNN, Rsenet18, SEresnet18 and Densenet121. Lastly, the location accuracies of GIALDN on CFRP laminates with the same thickness and different stacking sequences are investigated and a good model applicability can be observed.

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