论文检索

Shuman Zhuang, Zhihao Wu, Yuhong Chen, Zihan Fang, Jiali Yin, Ximeng Liu

Facing the growing diversity of multi-view data, multi-view graph-based models have made encouraging progress in handling multi-view data modeled as graphs. Graph Contrastive Learning (GCL) naturally fits multi-view graph data by treating their inherent views as augmentations. However, the development of GCL on multi-view graph data is still in the infant stage. Challenges remain in designing strategies that coordinate preprocessing and contrastive learning, and in developing model architectures that automatically meet the needs of diverse views. To tackle these, we propose a framework named CAMEL, which refines consistency learning by introducing a tailored contrastive paradigm for multi-view graphs. Initially, we theoretically analyze the positive effect of edge-dropping preprocessing on the consistency and quantify the factors that influence it. Paired with a learnable model architecture, the proposed adaptive edge-dropping preprocessing strategy is guided by dynamic topology, making the heterogeneity of views more controllable and better aligned with contrastive learning. Finally, we design a neighborhood consistency multi-view contrastive objective that enhances consistency information interaction by extending positive samples. Extensive experiments on downstream tasks, including node classification and clustering, validate the superiority of our proposed model.

Xiren Zhou, Ziyu Tang, Shikang Liu, Ao Chen, Xiangyu Wang, Huanhuan Chen

Fault Diagnosis (FD) in time-varying data presents considerations such as limited training data, intra- and inter-dimensional correlations, and constraints of training time. In response, this paper introduces FD in the Reservoir-Embedded-Directional Network (REDNet) model space. Model-oriented methods utilize well-fitted networks or functions, denoted as "models" that capture data's changing information, as more stable and parsimonious representations of the data. Our approach employs REDNet for data fitting, wherein multiple reservoirs are organized along intrinsic correlation directions to establish intra- and inter-dimensional dependencies, thereby capturing multi-directional dynamics in high-dimensional data. Representing each data instance with an independently fitted REDNet model maps these instances into a class-separable REDNet model space, where FD could be performed on the models rather than the original data. Concentrating on the data-intrinsic dynamics, our method achieves rapid training speeds, and maintains robust performance even with minimal training data. Experiments on several datasets demonstrate its effectiveness.

Xiaoling Zhou, Wei Ye, Zhemg Lee, Shikun Zhang

Spurious correlations pose a significant challenge to the robustness of statistical models, often resulting in unsatisfactory performance when distributional shifts occur between training and testing data. To address this, we propose to transfer knowledge across spuriously correlated categories within the deep feature space. Specifically, samples' deep features are enriched using semantic vectors extracted from both their respective category distributions and those of their spuriously correlated counterparts, enabling the generation of diverse class-specific factual and counterfactual augmented deep features. We then demonstrate the feasibility of optimizing a surrogate robust loss instead of conducting explicit augmentations by considering an infinite number of augmentations. As spurious correlations between samples and classes evolve during training, we develop a reinforcement learning-based training framework called Dynamic Knowledge Transfer (DKT) to facilitate dynamic adjustments in the direction and intensity of knowledge transfer. Within this framework, a target network is trained using the derived robust loss to enhance robustness, while a strategy network generates sample-wise augmentation strategies in a dynamic and automatic way. Extensive experiments validate the effectiveness of the DKT framework in mitigating spurious correlations, achieving state-of-the-art performance across three typical learning scenarios susceptible to such correlations.

Shiman Zhao, Shangyuan Li, Wei Chen, Tengjiao Wang, Jiahui Yao, Jiabin Zheng, Kam-Fai Wong

Few-shot Multi-label Intent Detection (MID) is crucial for dialogue systems, aiming to detect multiple intents of utterances in low-resource dialogue domains. Previous studies focus on a two-stage pipeline. They first learn representations of utterances with multiple labels and then use a threshold-based strategy to identify multi-label results. However, these methods rely on representation classification and ignore instance relations, leading to error propagation. To solve the above issues, we propose a multi-label joint learning method for few-shot MID in an end-to-end manner, which constructs an instance relation learning network with label knowledge propagation to eliminate error propagation. Concretely, we learn the interaction relations between instances with class information to propagate label knowledge between a few labeled (support set) and unlabeled (query set) instances. With label knowledge propagation, the relation strength between instances directly indicates whether two utterances belong to the same intent for multi-label prediction. Besides, a dual relation-enhanced loss is developed to optimize support- and query-level relation strength to improve performance. Experiments show that we outperform strong baselines by an average of 9.54% AUC and 11.19% Macro-F1 in 1-shot scenarios.

Xiaoyang Yu, Xiaoming Wu, Xin Wang, Dongrun Li, Ming Yang, Peng Cheng

Federated semantic segmentation enables pixel-level classification in images through collaborative learning while maintaining data privacy. However, existing research commonly overlooks the fine-grained class relationships within the semantic space when addressing heterogeneous problems, particularly domain shift. This oversight results in ambiguities between class representation. To overcome this challenge, we propose a novel federated segmentation framework that strikes class consistency, termed FedSaaS. Specifically, we introduce class exemplars as a criterion for both local- and global-level class representations. On the server side, the uploaded class exemplars are leveraged to model class prototypes, which supervise global branch of clients, ensuring alignment with global-level representation. On the client side, we incorporate an adversarial mechanism to harmonize contributions of global and local branches, leading to consistent output. Moreover, multilevel contrastive losses are employed on both sides to enforce consistency between two-level representations in the same semantic space. Extensive experiments on five driving scene segmentation datasets demonstrate that our framework outperforms state-of-the-art methods, significantly improving average segmentation accuracy and effectively addressing the class-consistency representation problem.

Yuya Yoshikawa, Masanari Kimura, Ryotaro Shimizu, Yuki Saito

Techniques that explain the predictions of black-box machine learning models are crucial to make the models transparent, thereby increasing trust in AI systems. The input features to the models often have a nested structure that consists of high- and low-level features, and each high-level feature is decomposed into multiple low-level features. For such inputs, both high-level feature attributions (HiFAs) and low-level feature attributions (LoFAs) are important for better understanding the model's decision. In this paper, we propose a model-agnostic local explanation method that effectively exploits the nested structure of the input to estimate the two-level feature attributions simultaneously. A key idea of the proposed method is to introduce the consistency property that should exist between the HiFAs and LoFAs, thereby bridging the separate optimization problems for estimating them. Thanks to this consistency property, the proposed method can produce HiFAs and LoFAs that are both faithful to the black-box models and consistent with each other, using a smaller number of queries to the models. In experiments on image classification in multiple instance learning and text classification using language models, we demonstrate that the HiFAs and LoFAs estimated by the proposed method are accurate, faithful to the behaviors of the black-box models, and provide consistent explanations.

Xiaomeng Ye, David Leake, Yu Wang, David Crandall

Deep neural networks have achieved remarkable performance across a variety of applications. However, their decision-making processes are opaque. In contrast, k-nearest neighbor (k-NN) provides interpretable predictions by relying on similar cases, but it lacks important capabilities of neural networks. The neural network k-nearest neighbor (NN-kNN) model is designed to bridge this gap, combining the benefits of neural networks with the instance-based interpretability of k-NN. However, the initial formulation of NN-kNN had limitations including scalability issues, reliance on surface-level features, and an excessive number of parameters. This paper improves NN-kNN by enhancing its scalability, parameter efficiency, ease of integration with feature extractors, and training simplicity. An evaluation of the revised architecture for image and language classification tasks illustrates its promise as a flexible and interpretable method.

Mingzheng Yang, Kai Zhang, Yuyang Ye, Yanghai Zhang, Runlong Yu, Min Hou

Multimodal sentiment analysis (MSA) has shown promising results but often poses significant challenges in real-world applications due to its dependence on the complete and aligned multimodal sequences. While existing approaches attempt to address missing modalities through feature reconstruction, they often neglect the complex interplay between homogeneous and heterogeneous relationships in multimodal features. To address this problem, we propose Decoupled-Adaptive Reconstruction (DAR), a novel framework that explicitly addresses these limitations through two key components: (1) a mutual information-based decoupling module that decomposes features into common and independent representations, and (2) a reconstruction module that independently processes these decoupled features before fusion for downstream tasks. Extensive experiments on two benchmark datasets demonstrate that DAR significantly outperforms existing methods in both modality reconstruction and sentiment analysis tasks, particularly in scenarios with missing or unaligned modalities. Our results show improvements of 2.21% in bi-classification accuracy and 3.9% in regression error compared to state-of-the-art baselines on the MOSEI dataset.

Xiaoshuo Yan, Zhaochuan Li, Lei Meng, Zhuang Qi, Wei Wu, Zixuan Li, Xiangxu Meng

Causal inference has emerged as a promising approach to mitigate long-tail classification by handling the biases introduced by class imbalance. However, along with the change of advanced backbone models from Convolutional Neural Networks (CNNs) to Visual Transformers (ViT), existing causal models may not achieve an expected performance gain. This paper investigates the influence of existing causal models on CNNs and ViT variants, highlighting that ViT's global feature representation makes it hard for causal methods to model associations between fine-grained features and predictions, which leads to difficulties in classifying tail classes with similar visual appearance. To address these issues, this paper proposes TSCNet, a two-stage causal modeling method to discover fine-grained causal associations through multi-scale causal interventions. Specifically, in the hierarchical causal representation learning stage (HCRL), it decouples the background and objects, applying backdoor interventions at both the patch and feature level to prevent model from using class-irrelevant areas to infer labels which enhances fine-grained causal representation. In the counterfactual logits' bias calibration stage (CLBC), it refines the optimization of model's decision boundary by adaptive constructing counterfactual balanced data distribution to remove the spurious associations in the logits caused by data distribution. Extensive experiments conducted on various long-tail benchmarks demonstrate that the proposed TSCNet can eliminate multiple biases introduced by data imbalance, which outperforms existing methods.

Yi Xu, Shuang Wang, Hantong Xing, Chenxu Wang, Dou Quan, Rui Yang, Dong Zhao, Luyang Mei

Deep learning methods have demonstrated remarkable performance across various communication signal processing tasks. However, most signal classification methods require a substantial amount of labeled samples for training, posing significant challenges in the field of communication signals, as labeling necessitates expert knowledge. This paper proposes a novel self-supervised signal classification method called Spectral-Guided Self-Supervised Signal Classification (SGSSC). Specifically, to leverage frequency-domain information with modulation semantics as prior knowledge for the model, we design a previously unexplored pretext task tailored to the format of signal data. This task involves predicting spectral information from masked time-domain signals, enabling the model to learn implicit signal features through cross-domain pattern transformation. Furthermore, the pretext task in the SGSSC method is relevant to the downstream classification task, and using traditional fine-tuning strategies on the downstream task may lead to the loss of certain features associated with the pretext task. Therefore, we propose an attention mechanism-based fine-tuning strategy that adaptively integrates pre-trained features from different levels. Extensive experimental results validate the superiority of the SGSSC method. For instance, when the proportion of labeled samples is only 0.5%, our method achieves an average improvement of 2.3% in downstream classification tasks compared to the best-performing self-supervised training strategies.

Fan Xu, Wuyang Chen, Wei Gao

Decision trees and forests have achieved successes in various real applications, most working with all testing classes known in training data. In this work, we focus on learning with augmented class via forests, where an augmented class may appear in testing data yet not in training data. We incorporate information of augmented class into trees' splitting, that is, augmented Gini impurity, a new splitting criterion is introduced to exploit some unlabeled data from testing distribution. We then develop the Learning with Augmented Class via Forests (short for LACForest) approach, which constructs shallow forests according to the augmented Gini impurity and then splits forests with pseudo-labeled augmented instances for better performance. We also develop deep neural forests via an optimization objective based on our augmented Gini impurity, which essentially utilizes the representation power of neural networks for forests. Theoretically, we present the convergence analysis for our augmented Gini impurity, and we finally conduct experiments to evaluate our approaches. The code is available at https://github.com/nju-xuf/LACForest.

Shuyin Xia, Xiaojiang Tian, Suzhen Yuan, Jeremiah D. Deng

High time complexity is one of the biggest challenges faced by k-Nearest Neighbors (kNN). Although current classical and quantum kNN algorithms have made some improvements, they still have a speed bottleneck when facing large amounts of data. To address this issue, we propose an innovative algorithm called Granular-Ball based Quantum kNN(GB-QkNN). This approach achieves higher efficiency by first employing granular-balls, which reduces the data size needed to processed. The search process is then accelerated by adopting a Hierarchical Navigable Small World (HNSW) method. Moreover, we optimize the time-consuming steps, such as distance calculation, of the HNSW via quantization, further reducing the time complexity of the construct and search process. By combining the use of granular-balls and quantization of the HNSW method, our approach manages to take advantage of these treatments and significantly reduces the time complexity of the kNN-like algorithms, as revealed by a comprehensive complexity analysis.

Benyu Wu, Wei Du, Jun Wang, Guoxian Yu

Incomplete multi-view data presents a significant challenge for multi-view clustering (MVC). Existing incomplete MVC solutions commonly rely on data imputation to convert incomplete data into complete data. However, this paradigm suffers from the risk of error accumulation when clustering unreliable imputed data, causing suboptimal clustering performance. Moreover, using imputation to fulfill missing data is inefficient, while inferring data categories based solely on the existing views is extremely challenging. To this end, we propose an Imputation-free Incomplete MVC (I2MVC) via pseudo-supervised knowledge distillation. Specifically, I2MVC decomposes the incomplete MVC problem into two tasks: an MVC task for complete data and a pseudo-supervised classification task for fully incomplete data. A self-supervised simple contrastive Teacher network is trained for clustering complete data, and its knowledge is distilled into a lightweight pseudo-supervised Student network. The Student network, unrestricted by view completeness, further guides the clustering of fully incomplete data. Finally, the clustering results from both tasks are merged to generate the final clustering outcome. Experimental results on benchmark datasets demonstrate the effectiveness of I2MVC.

Hyowon Wi, Jeongwhan Choi, Noseong Park

Transformers have demonstrated remarkable performance across diverse domains. The key component of Transformers is self-attention, which learns the relationship between any two tokens in the input sequence. Recent studies have revealed that the self-attention can be understood as a normalized adjacency matrix of a graph. Notably, from the perspective of graph signal processing (GSP), the self-attention can be equivalently defined as a simple graph filter, applying GSP using the value vector as the signal. However, the self-attention is a graph filter defined with only the first order of the polynomial matrix, and acts as a low-pass filter preventing the effective leverage of various frequency information. Consequently, existing self-attention mechanisms are designed in a rather simplified manner. Therefore, we propose a novel method, called Attentive Graph Filter (AGF), interpreting the self-attention as learning the graph filter in the singular value domain from the perspective of graph signal processing for directed graphs with the linear complexity w.r.t. the input length. In our experiments, we demonstrate that AGF achieves state-of-the-art performance on various tasks, including Long Range Arena benchmark and time series classification. Code is available at https://github.com/hyowonwi/agf.

Yumeng Wang, Zengyi Wo, Wenjun Wang, Xingcheng Fu, Minglai Shao

Graph Neural Networks (GNNs) excel in node classification tasks but often assume homophily, where connected nodes share similar labels. This assumption does not hold in many real-world heterophilic graphs. Existing models for heterophilic graphs primarily rely on pairwise relationships, overlooking multi-scale information from higher-order structures. This leads to suboptimal performance, particularly under noise from conflicting class information across nodes. To address these challenges, we propose HPGNN, a novel model integrating Higher-order Personalized PageRank with Graph Neural Networks. HPGNN introduces an efficient high-order approximation of Personalized PageRank (PPR) to capture long-range and multiscale node interactions. This approach reduces computational complexity and mitigates noise from surrounding information. By embedding higher-order structural information into convolutional networks, HPGNN effectively models key interactions across diverse graph dimensions. Extensive experiments on benchmark datasets demonstrate HPGNN’s effectiveness. The model achieves better performance than five out of seven state-of-the-art methods on heterophilic graphs in downstream tasks while maintaining competitive performance on homophilic graphs. HPGNN’s ability to balance multi-scale information and robustness to noise makes it a versatile solution for real-world graph learning challenges. Codes are available at https://github.com/streetcorner/HPGNN.

Yi Wang, Changqin Huang, Ming Li, Tingyi Cai, Zhonglong Zheng, Xiaodi Huang

Heterophilic graph neural networks (GNNs) have gained prominence for their ability to learn effective representations in graphs with diverse, attribute-aware relationships. While existing methods leverage attribute inference during message passing to improve performance, they often struggle with challenging heterophilic graphs. This is due to edge distribution shifts introduced by diverse connection patterns, which blur attribute distinctions and undermine message-passing stability. This paper introduces H₂OGNN, a novel framework that reframes edge attribute inference as an out-of-distribution (OOD) detection problem. H₂OGNN introduces a simple yet effective symbolic energy regularization approach for OOD learning, ensuring robust classification boundaries between homophilic and heterophilic edge attributes. This design significantly improves the stability and reliability of GNNs across diverse connectivity patterns. Through theoretical analysis, we show that H₂OGNN addresses the graph denoising problem by going beyond feature smoothing, offering deeper insights into how precise edge attribute identification boosts model performance. Extensive experiments on nine benchmark datasets demonstrate that H₂OGNN not only achieves state-of-the-art performance but also consistently outperforms other heterophilic GNN frameworks, particularly on datasets with high heterophily.

Yangchuan Wang, Lianhong Ding, Peng Shi

Adaptive gradient methods, primarily based on Adam, are prevalent in training neural networks, adjusting step sizes via exponentially decaying averages of gradients and squared gradients. Adam assigns small weights to distant gradients, termed long-tail gradients in this paper. However, these gradients persistently influence update behavior, potentially degrading generalization performance. To address this issue, we incorporate a restart mechanism into moment estimations, proposing AdaR (ADAptive gradient methods via Restarting moment estimations). Specifically, AdaR divides a training epoch into fixed-iteration intervals, alternating between two sets of moment estimations for parameter updates and discarding prior moment estimations at the beginning of each interval. Within each interval, one set updates parameters and will be discarded in the subsequent interval, while the other is reset at the midpoint to estimate moments for updates in the subsequent interval. The restart mechanism cyclically discards distant gradients, initiates fresh moment estimations for parameter updates, and stabilizes training. By prioritizing recent gradients, the method increases estimation accuracy and enhances step size adjustment. Empirically, AdaR outperforms state-of-the-art optimization algorithms on image classification and language modeling tasks, demonstrating superior generalization and faster convergence.

Xuyang Wang, Siyuan Duan, Qizhi Li, Guiduo Duan, Yuan Sun, Dezhong Peng

Trustworthy multi-view learning has attracted extensive attention because evidence learning can provide reliable uncertainty estimation to enhance the credibility of multi-view predictions. Existing trusted multi-view learning methods implicitly assume that multi-view data is secure. However, in safety-sensitive applications such as autonomous driving and security monitoring, multi-view data often faces threats from adversarial perturbations, thereby deceiving or disrupting multi-view models. This inevitably leads to the adversarial unreliability problem (AUP) in trusted multi-view learning. To overcome this tricky problem, we propose a novel multi-view learning framework, namely Reliable Disentanglement Multi-view Learning (RDML). Specifically, we first propose evidential disentanglement learning to decompose each view into clean and adversarial parts under the guidance of corresponding evidences, which is extracted by a pretrained evidence extractor. Then, we employ the feature recalibration module to mitigate the negative impact of adversarial perturbations and extract potential informative features from them. Finally, to further ignore the irreparable adversarial interferences, a view-level evidential attention mechanism is designed. Extensive experiments on multi-view classification tasks with adversarial attacks show that RDML outperforms the state-of-the-art methods by a relatively large margin. Our code is available at https://github.com/Willy1005/2025-IJCAI-RDML.

Wuyuqing Wang, Erkun Yang, Zilan Zhou, Cheng Deng

In-context learning (ICL), a paradigm derived from large language models (LLMs), holds significant promise but is notably sensitive to the choice of input demonstrations. While numerous methodologies have been developed to select the optimal demonstrations from existing datasets, our work alternatively proposes to generate representative demonstrations through a Distillation-based Demonstration Generation (DDG) framework. Specifically, our approach aims to generate demonstrations that encapsulate the essential attributes of the target dataset. Rather than optimizing these demonstrations directly, we design a generative model and try to refine it by minimizing the discrepancies between the calculative models trained on generated demonstrations and the original datasets respectively. Additionally, we leverage a teacher-student framework to stabilize the training process and improve the quality of the synthesized samples. Extensive experiments conducted across ten prevalent text datasets demonstrate that our DDG method substantially outperforms existing state-of-the-art methodologies. Our code will be available at https://github.com/wwyq1/DDG.

Ren Wang, Haoliang Sun, Yuxiu Lin, Xinxin Zhang, Yilong Yin

Meta-learning methods typically follow a two-loop framework, where each loop potentially suffers from notorious overfitting, hindering rapid adaptation and generalization to new tasks. Existing methods address this by enhancing the mutual-exclusivity or diversity of training samples, but these data manipulation strategies are data-dependent and insufficiently flexible. This work proposes a data-independent Meta-Gradient Augmentation (MGAug) method from the perspective of gradient regularization. The key idea is first to break the rote memories by network pruning to address memorization overfitting in the inner loop, then use the gradients of pruned sub-networks to augment meta-gradients, alleviating overfitting in the outer loop. Specifically, we explore three pruning strategies, including random width pruning, random parameter pruning, and a newly proposed catfish pruning that measures a Meta-Memorization Carrying Amount (MMCA) score for each parameter and prunes high-score ones to break rote memories. The proposed MGAug is theoretically guaranteed by the generalization bound from the PAC-Bayes framework. Extensive experiments on multiple few-shot learning benchmarks validate MGAug's effectiveness and significant improvement over various meta-baselines.