Author name cluster

Boyu Wang

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.

36 papers

2 author rows

JBHI Journal 2026 Journal Article

Dual-Branch Attention-Based Frequency Domain Network for Cross-Subject SSVEP-BCIs

Yi Yang
Ze Wang
Ziyu Jia
Boyu Wang
Shangen Zhang
Chi Man Wong
Xiaorong Gao
Tzyy-Ping Jung

Steady-state visual evoked potential-based brain-computer interfaces (SSVEP-BCIs) hold significant promise for enabling high-speed human-computer interaction in real-world scenarios. However, existing frequency-domain decoding methods treat frequency spectrum features (the real and imaginary spectrum features) as a single feature without considering their unique spatial and spectral characteristics, resulting in insufficient generalizable features and limited classification accuracy in cross-subject scenarios. To address this issue, we propose a Dual-Branch Attention-Based Frequency Domain Network (DB-AFDNet) to independently decode real and imaginary spectral components, aiming to acquire more discriminative and generalizable features for cross-subject applications. Specifically, we construct inter-branch attention similarity constraints to encourage the two branches to have similar attention properties, promoting to learn the consensus characteristics in the dual branches. Furthermore, we propose intra-branch orthogonality constraints to explore branch-specific discriminative features to learn generalizable features. Experimental studies on two public datasets, the Benchmark and Beta datasets, demonstrate that DB-AFDNet outperforms state-of-the-art methods in cross-subject classification, achieving a relative improvement of 1. 36 $\%$ and 1. 45 $\%$, respectively.

Details DOI

AAAI Conference 2026 Conference Paper

Graph Domain Adaptation via Homophily-Agnostic Reconstructing Structure

Ruiyi Fang
Shuo Wang
Ruizhi Pu
Qiuhao Zeng
Hao Zheng
Ziyan Wang
Jiale Cai
Zhimin Mei

Graph Domain Adaptation (GDA) transfers knowledge from labeled source graphs to unlabeled target graphs, addressing the challenge of label scarcity. However, existing GDA methods typically assume that both source and target graphs exhibit homophily, leading existing methods to perform poorly when heterophily is present. Furthermore, the lack of labels in the target graph makes it impossible to assess its homophily level beforehand. To address this challenge, we propose a novel homophily-agnostic approach that effectively transfers knowledge between graphs with varying degrees of homophily. Specifically, we adopt a divide-and-conquer strategy that first separately reconstructs highly homophilic and heterophilic variants of both the source and target graphs, and then performs knowledge alignment separately between corresponding graph variants. Extensive experiments conducted on five benchmark datasets demonstrate the superior performance of our approach, particularly highlighting its substantial advantages on heterophilic graphs.

PDF Details DOI

AAAI Conference 2026 Conference Paper

HiFC-GAN: Hierarchical Feature-Constrained GAN for Optical-to-SAR Transfer in SAR Target Classification

Hao Zheng
Meiguang Zheng
Zhigang Hu
Liu Yang
Aikun Xu
Tingxuan Chen
Rongchang Zhao
Boyu Wang

The limited availability of high-quality training data poses a persistent challenge for synthetic aperture radar (SAR) target classification. Existing data augmentation methods mainly adopt a simplistic application of GAN-based style transfer techniques to directly synthesize pseudo-SAR images from optical images. However, our in-depth analysis of this cross-modal conversion reveals that such straightforward strategies primarily focus on transferring high-level semantic information (e.g., target shapes), thus failing to adequately capture the essential low-level features unique to SAR imagery (e.g., scattering textures). To address this inherent trade-off between high-level semantic preservation and low-level feature authenticity, we propose a Hierarchical Feature-Constrained GAN (HiFC-GAN) tailored for optical-to-SAR style transfer. Specifically, HiFC-GAN enhances the representation of low-level SAR features by introducing local texture contrast constraints at shallow layers, while introducing explicit feature mapping constraints at deeper layers to maintain high-level semantic consistency throughout the reconstruction process. Experimental results demonstrate that HiFC-GAN significantly outperforms existing GAN-based techniques in image generation quality, particularly improving the low-level feature authenticity of pseudo-SAR images. Moreover, the generated pseudo-SAR images further improve the performance of downstream target classification tasks, yielding accuracy gains ranging from 3.56% to 5.90% on average with mainstream CNN-based models.

PDF Details DOI

AAAI Conference 2026 Conference Paper

Intra-Class Unbiased Prototype Aggregation and Classifier Collaboration for Personalized Federated Learning

Hao Zheng
Shiyu Song
Zhigang Hu
Meiguang Zheng
Liu Yang
Aikun Xu
Rongchang Zhao
Ruizhi Pu

Prototype-based personalized federated learning methods have emerged as a promising strategy due to their ability to represent client-specific class characteristics effectively through learned class prototypes. These prototypes capture salient features of client-local data, facilitating personalized model adaptation. However, existing prototype-based aggregation strategies predominantly rely on weighted averaging, implicitly assuming prototype consistency across clients. This assumption neglects the intrinsic heterogeneity and non-independent and identically distributed (non-IID) nature of client data, compelling diverse local prototypes to align toward a singular global prototype and consequently causing significant aggregation bias. Motivated by observations from intra-class feature saliency analysis, we identify that clients inherently emphasize distinct feature regions even for the same class. To leverage this intra-class diversity, we introduce FedIC, a novel prototype clustering and collaborative classifier optimization approach. Specifically, FedIC first clusters prototypes based on intra-class similarity to form intra-class prototype subspaces, ensuring that aggregation occurs exclusively within each cluster, thus eliminating the bias stemming from forced global unification. To further exploit the benefits of intra-cluster collaboration, we quantify the combined predictive gains of classifiers from clients within the same cluster as a function of classifier combination weights. This targeted aggregation and collaborative optimization strategy effectively circumvents the bias introduced by global alignment. Extensive experiments under various non-IID settings show that FedIC significantly outperforms existing Prototype-based and Clustered PFL Methods.

PDF Details DOI

JBHI Journal 2026 Journal Article

Unified Online Adaptation Framework for Correlation Analysis-based Spatial Filtering Methods in SSVEP-based BCIs

Ze Wang
Lu Shen
Xinran Mi
Leqian Cheng
Yi Yang
Boyu Wang
Tzyy-Ping Jung
Feng Wan

Online adaptation is a promising technique for achieving calibration-free recognition in user-friendly brain-computer interfaces (BCIs) but remains underexplored for steady-state visual evoked potential (SSVEP) recognition. In our previous work on online multi-stimulus canonical correlation analysis (OMSCCA), we introduced a state-of-the-art scheme for the online adaptation of SSVEP spatial filters. Despite its effectiveness, this approach can not be directly extended to other advanced spatial filtering methods, thereby seriously limiting the broader development of calibration-free algorithms. To address this limitation, we propose a unified online adaptation frame work for correlation analysis (CA)-based spatial filtering methods, encompassing both spatial filter computation and utilization. Specifically, we extend the least-squares (LS) unified framework originally designed for full calibration with large amounts of training data to the online adaptation scenario without any pre-calibration, thereby enabling continuous updates of spatial filters. Moreover, to sufficiently utilize spatial filters, we introduce a cross-stimulus transfer method for online adaptation of the common impulse response and generation of user-specific templates for all stimuli using limited online unlabeled data. Finally, leveraging the proposed unified framework, we adapt three advanced spatial filtering methods from their calibration based counter parts to online adaptation paradigms and validate their performance through simulation studies. Our results demonstrate the framework's effectiveness in promoting the development ofzero-calibration SSVEP-based BCIs. Compared to the OMSCCA, the proposed online adaptation methods canimprove the recognition performance by more than 12%. This work provides a generalizable approach for transforming existing calibration-based methods into adaptive, user-friendly solutions for practical BCI applications.

Details DOI

AAAI Conference 2025 Conference Paper

ConFREE: Conflict-free Client Update Aggregation for Personalized Federated Learning

Hao Zheng
Zhigang Hu
Liu Yang
Meiguang Zheng
Aikun Xu
Boyu Wang

Negative transfer (NF) is a critical challenge in personalized federated learning (pFL). Existing methods primarily focus on adapting local data distribution on the client side, which can only resist NF, rather than avoid NF itself. To tackle NF at its root, we investigate its mechanism through the lens of the global model, and argue that it is caused by update conflicts among clients during server aggregation. In light of this, we propose a conflict-free client update aggregation strategy (ConFREE), which enables us to avoid NF in pFL. Specifically, ConFREE guides the global update direction by constructing a conflict-free guidance vector through projection and utilizes the optimal local improvements of the worst-performing clients near the guidance vector to regularize server aggregation. This prevents the conflicting components of updates from transferring, achieving balanced updates across different clients. Notably, ConFREE is model-agnostic and can be straightforwardly adopted as a complement to enhance various existing NF-resistance methods implemented on the client side. Extensive experiments demonstrate substantial improvements to existing pFL algorithms by leveraging ConFREE.

PDF Details DOI

AAAI Conference 2025 Conference Paper

Leveraging Group Classification with Descending Soft Labeling for Deep Imbalanced Regression

Ruizhi Pu
Gezheng Xu
Ruiyi Fang
Bing-Kun Bao
Charles Ling
Boyu Wang

Deep imbalanced regression (DIR), where the target values have a highly skewed distribution and are also continuous, is an intriguing yet under-explored problem in machine learning. While recent works have already shown that incorporating various classification-based regularizers can produce enhanced outcomes, the role of classification remains elusive in DIR. Moreover, such regularizers (e.g., contrastive penalties) merely focus on learning discriminative features of data, which inevitably results in ignorance of either continuity or similarity across the data. To address these issues, we first bridge the connection between the objectives of DIR and classification from a Bayesian perspective. Consequently, this motivates us to decompose the objective of DIR into a combination of classification and regression tasks, which naturally guides us toward a divide-and-conquer manner to solve the DIR problem. Specifically, by aggregating the data at nearby labels into the same groups, we introduce an ordinal group-aware contrastive learning loss along with a multi-experts regressor to tackle the different groups of data thereby maintaining the data continuity. Meanwhile, considering the similarity between the groups, we also propose a symmetric descending soft labeling strategy to exploit the intrinsic similarity across the data, which allows classification to facilitate regression more effectively. Extensive experiments on real-world datasets also validate the effectiveness of our method.

PDF Details DOI

AAAI Conference 2025 Conference Paper

MABR: Multilayer Adversarial Bias Removal Without Prior Bias Knowledge

Maxwell J. Yin
Boyu Wang
Charles Ling

Models trained on real-world data often mirror and exacerbate existing social biases. Traditional methods for mitigating these biases typically require prior knowledge of the specific biases to be addressed, and the social groups associated with each instance. In this paper, we introduce a novel adversarial training strategy that operates withour relying on prior bias-type knowledge (e.g., gender or racial bias) and protected attribute labels. Our approach dynamically identifies biases during model training by utilizing auxiliary bias detector. These detected biases are simultaneously mitigated through adversarial training. Crucially, we implement these bias detectors at various levels of the feature maps of the main model, enabling the detection of a broader and more nuanced range of bias features. Through experiments on racial and gender biases in sentiment and occupation classification tasks, our method effectively reduces social biases without the need for demographic annotations. Moreover, our approach not only matches but often surpasses the efficacy of methods that require detailed demographic insights, marking a significant advancement in bias mitigation techniques.

PDF Details DOI

ICLR Conference 2025 Conference Paper

Revisiting Source-Free Domain Adaptation: a New Perspective via Uncertainty Control

Gezheng Xu
Hui Guo
Li Yi
Charles X. Ling
Boyu Wang
Grace Yi

Source-Free Domain Adaptation (SFDA) seeks to adapt a pre-trained source model to the target domain using only unlabeled target data, without access to the original source data. While current state-of-the-art (SOTA) methods rely on leveraging weak supervision from the source model to extract reliable information for self-supervised adaptation, they often overlook the uncertainty that arises during the transfer process. In this paper, we conduct a systematic and theoretical analysis of the uncertainty inherent in existing SFDA methods and demonstrate its impact on transfer performance through the lens of Distributionally Robust Optimization (DRO). Building upon the theoretical results, we propose a novel instance-dependent uncertainty control algorithm for SFDA. Our method is designed to quantify and exploit the uncertainty during the adaptation process, significantly improving the model performance. Extensive experiments on benchmark datasets and empirical analyses confirm the validity of our theoretical findings and the effectiveness of the proposed method. This work offers new insights into understanding and advancing SFDA performance.

Details

AAAI Conference 2025 Conference Paper

Textualize Visual Prompt for Image Editing via Diffusion Bridge

Pengcheng Xu
Qingnan Fan
Fei Kou
Shuai Qin
Hong Gu
Ruoyu Zhao
Charles Ling
Boyu Wang

Visual prompt, a pair of before-and-after edited images, can convey indescribable imagery transformations and prosper in image editing. However, current visual prompt methods rely on a pretrained text-guided image-to-image generative model that requires a triplet of text, before, and after images for retraining over a text-to-image model. Such crafting triplets and retraining processes limit the scalability and generalization of editing. In this paper, we present a framework based on any single text-to-image model without reliance on the explicit image-to-image model thus enhancing the generalizability and scalability. Specifically, by leveraging the probability-flow ordinary equation, we construct a diffusion bridge to transfer the distribution between before-and-after images under the text guidance. By optimizing the text via the bridge, the framework adaptively textualizes the editing transformation conveyed by visual prompts into text embeddings without other models. Meanwhile, we introduce differential attention control during optimization, which disentangles the text embedding from the invariance of the before-and-after images and makes it solely capture the delicate transformation and generalize to edit various images. Experiments on real images validate competitive results on the generalization, contextual coherence, and high fidelity for delicate editing with just one image pair as the visual prompt.

PDF Details DOI

IJCAI Conference 2025 Conference Paper

Towards Debiased Generalized Category Discovery

Pengcheng Guo
Yonghong Song
Boyu Wang

Generalized Category Discovery (GCD) aims at classifying unlabeled training data coming from old and novel classes by leveraging the information of partially labeled old classes. In this paper, we reveal that existing methods often suffer from competition between new and old classes, where the focus on learning new classes often results in a notable performance degradation on the old classes. Moreover, we delve into the reason behind this problem: the GCD classifier can be overconfident and biased towards the new class. With this insight, we propose Debiased GCD (DeGCD), a simple but effective approach that mitigates the bias caused by the overconfidence from new categories by a debiased head. Specifically, we first propose semantic calibration loss that aids the GCD classifier in debiasing by enforcing neighborhood prediction consistency with the latent representation of the debiased head. Furthermore, a debiased contrastive objective is proposed to refine the similarity matrix from the GCD classifier and the debiased classifier, suppressing the overconfidence in new classes in unlabeled data. In addition, an alignment constraint loss is designed to prevent damaging the distribution of the old categories caused by overconfidence in the new categories. Experiments on various datasets shows DeGCD achieves state-of-the-art performance and maintains a good balance between new and old classes. In addition, this method can be seamlessly adapted to other GCD methods, not only to achieve further performance gains but also to effectively balance the performance of the new class with that of the old class.

PDF Details DOI

TMLR Journal 2025 Journal Article

Uniform Noise Distribution and Compact Clusters: Unveiling the Success of Self-Supervised Learning in Label Noise

Pengcheng Xu
Li Yi
Gezheng Xu
Xi Chen
Ian McLeod
Charles Ling
Boyu Wang

Label noise is ubiquitous in real-world datasets, posing significant challenges to machine learning models. While self-supervised learning (SSL) algorithms have empirically demonstrated effectiveness in learning noisy labels, the theoretical understanding of their effectiveness remains underexplored. In this paper, we present a theoretical framework to understand how SSL methods enhance learning with noisy labels, especially for the instance-dependent label noise. We reveal that the uniform and compact cluster structures induced by contrastive SSL play a crucial role in mitigating the adverse effects of label noise. Specifically, we theoretically show that a classifier trained on SSL-learned representations significantly outperforms one trained using traditional supervised learning methods. This results from two key merits of SSL representations over label noise: 1. Uniform Noise Distribution: Label noise becomes uniformly distributed over SSL representations with respect to the true class labels, rather than the noisy ones, leading to an easier learning task. 2. Enhanced Cluster Structure: SSL enhances the formation of well-separated and compact categorical clusters, increasing inter-class distances while tightening intra-class clusters. We further theoretically justify the benefits of training a classifier on such structured representations, demonstrating that it encourages the classifier trained on noisy data to be aligned with the optimal classifier. Extensive experiments validate the robustness of SSL representations in combating label noise, confirming the practical values of our theoretical findings.