The ubiquitous and unavoidable label noise brings great challenges to the generalization performance of learning methods.Label noise correction aims to detect and correct label noise in the data, which is one of the most potential methods to address this challenge.Current methods for label noise filtering that utilize primitive features primarily concentrate on identifying noise, which often limits their capacity to adaptively learn features crucial for specific tasks, thereby resulting in a higher rate of noise identification within the noise recognition process. On the other hand, deep neural networks, endowed with robust feature extraction capabilities, typically exhibit lower noise identification, as they are prone to fitting noise patterns during the recognition process, potentially undermining their overall efficacy. Moreover, Fuzzy Learning Machine (FLM) excels not only in feature extraction but also in noise tolerance, adeptly navigating data uncertainties. FLM enhances the accuracy of the labels by calculating the membership degrees of samples across categories and determining their fuzzy memberships. The introduction of a two-stage FLM-based framework, which employs a secondary learning mechanism for precise noise filtering and correction, has shown substantial improvements in noise correction across various large-scale noisy datasets, thereby significantly enhancing samples' quality and boosting the generalization capabilities of classifiers.

Deep learning has achieved remarkable success in supervised image classification tasks, which relies on a large number of labeled samples for each class. Recently, zero-shot learning has garnered significant attention, which aims to recognize unseen classes using only training samples from seen classes. To bridge the gap between images and classes, class semantic attributes are introduced, making the alignment between image and class semantic attributes critical to zero-shot learning. However, existing methods often struggle to accurately focus on the image regions corresponding to individual class semantic attributes and tend to overlook the relations between different regions of an image, leading to poor alignment. To address these challenges, we propose a class semantic attribute perception guided zero-shot learning method. Specifically, we achieve coarse-grained perception of class semantic attributes across the entire image through contrastive semantic learning. Additionally, we attain fine-grained perception of individual class semantic attributes within image regions via region partitioning-based attribute alignment, which fully considers the relations between different regions of an image. By integrating these two processes into a unified network, we achieve multi-grained class semantic attribute perception, thereby enhancing the alignment between images and class semantic attributes. We validate the effectiveness of the proposed method on zero-shot learning benchmark data sets.

Traditional semi-supervised learning achieves significant success in closed-world scenarios. To better align with the openness of the real world, researchers propose open-world semi-supervised learning (OWSSL), which enables models to effectively recognize known and unknown classes even without labels for unknown classes. Recently, researchers have attempted to enhance the model performance in recognizing visually similar classes by integrating textual information. However, these attempts do not effectively align images with text, resulting in limited improvements in model performance. In response to this challenge, we propose a novel OWSSL method. By adopting a global-and-local textual prompt learning strategy to enhance image-text alignment effectiveness, and implementing a forward-and-backward strategy to reduce noise in image-text matching for unlabeled samples, we ultimately enhance the model’s ability to extract and recognize discriminative features across different classes. Experimental results on multiple fine-grained datasets demonstrate that our method achieves significant performance improvements compared to state-of-the-art methods.

Convolutional neural networks (CNNs) excel at learning spatial patterns through local receptive fields, while channel attention mechanisms enhance feature discrimination by emphasizing informative channels. Traditional approaches like SENet generate channel weights through global pooling and bottlenecked MLPs, but fundamentally ignore cross-channel dependencies—a critical limitation that discards contextual relationships between features. To address this, we propose the Cross-Channel Graph Attention mechanism (CCA), which explicitly models channel interactions via graph neural networks. Specifically, CCA represents channels as graph nodes, constructs sparse KNN graphs to capture inter-channel dependencies, and performs attention-based feature aggregation. Theoretically, we prove that CCA strictly expands the expressive power of conventional channel attention through full-rank attention matrices, while maintaining superior generalization via sparsity-induced Rademacher complexity bounds. Furthermore, we establish that CCA can universally approximate any continuous channel mixing function with controlled neighbor size (Theorem 3). Extensive experiments on image classification (CIFAR-10/100, mini-ImageNet) and super-resolution (Set5/14) validate that CCA-equipped networks achieve state-of-the-art performance—improving ResNet34 by 2.16% TOP-1 accuracy on CIFAR-10 and boosting VDSR’s PSNR by 0.43dB on ×3 scaling. Both theoretical and empirical analyses demonstrate that CCA achieves an optimal balance between feature interaction capacity and computational efficiency through its graph-structured attention paradigm. The code is available at https://github.com/openreview-pro/CCA

Classification is a cornerstone of machine learning research. Most of the existing classifiers assume that the concepts corresponding to classes can be precisely defined. This notion diverges from the widely accepted understanding in cognitive science, which posits that real-world concepts are often inherently ambiguous. To bridge this big gap, we propose a Human Cognition-Inspired Hierarchical Fuzzy Learning Machine (HC-HFLM), which leverages a novel hierarchical alignment loss to integrate rich class knowledge from human knowledge system into learning process. We further theoretically prove that minimizing this loss can align the hierarchical structure derived from data with those contained in class knowledge, resulting in clear semantics and high interpretability. Systematic experiments verify that the proposed method can achieve significant gains in interpretability and generalization performance.

Open-world semi-supervised learning (OWSSL) aims to recognize both known and unknown classes, but the labeled samples only cover the known classes. Existing OWSSL methods primarily represent classes as symbolic variables, which ignore the rich internal semantic information associated with the classes and thus hampers their ability to recognize unknown classes. Recent studies incorporate textual descriptions of classes to facilitate training, but these methods overlook the class semantic correlations, which constrains their effectiveness in recognizing unknown classes. To address these issues, we propose a novel OWSSL method. Our method fine-tunes only the image encoder during training while keeping the text encoder frozen, thereby preserving the rich semantic correlations learned during the pre-training phase. Furthermore, we employ a semantic margin to extract class semantic correlations from textual descriptions, which are then utilized in enhancing image representation discriminability. Experimental results across multiple datasets demonstrate that our method significantly outperforms representative OWSSL methods in the recognition of both known and unknown classes.

The success of current graph contrastive learning methods largely relies on the choice of data augmentation and contrastive objectives. However, most existing methods tend to optimize these two components independently, neglecting their potential interplay, which leads to suboptimal quality of the learned embeddings. To address this issue, we propose Uncertainty-guided Graph Contrastive Learning (UGCL) from a unified perspective. The core of our method is the introduction of sample uncertainty, a critical metric that quantifies the degree of class ambiguity within individual samples. On this basis, we design a novel multi-scale data augmentation strategy and a weighted graph contrastive loss function, both of which significantly enhance the quality of embeddings. Theoretically, we demonstrate that UGCL can coordinate overall optimization objectives through uncertainty, and through experiments, we show that it improves the performance of tasks such as node classification, node clustering, and link prediction, thereby verifying the effectiveness of our method.

The generalization performance of machine learning methods depends heavily on the quality of data representation. However, existing researches rarely consider representation learning from the perspective of generalization error. In this paper, we prove that generalization error of representation learning function can be estimated effectively by solving two convex optimization problems. Based on it, we propose a general representation learning framework. And then, we apply the proposed framework to two most commonly used nonlinear mapping methods, i.e., kernel based method and deep neural network (DNN), and thus design a kernel selection method and a DNN boosting framework, correspondingly. Finally, extensive experiments verify the effectiveness of the proposed methods.

The majority of the classical dimensionality reduction methods can be unified into a graph-embedding-based framework. A fixed graph constructed in a high-dimensional space has been extensively employed in the graph-embedding-based dimensionality reduction methods. However, a fixed graph often cannot characterize the structure of high-dimensional data owing to the curse of dimensionality. To solve this problem, we combine graph construction and dimensionality reduction into a coherent framework. Thus, the constructed graph can be updated dynamically in dimensionality reduction. In the existing methods based on the coherent framework, graphs are usually constructed by a type of neighborhood relationship and single clustering result. This study proposes an unsupervised dimensionality reduction method guided by fusing multiple clustering results. In the proposed method, multiple clustering results are first obtained by the k-means algorithm, and then a graph is constructed using a weighted co-association matrix of fusing the clustering results to capture data distribution information. Based on the graph, we present an objective function of combining graph construction and dimensionality reduction to implement mutual guidance between them. Numerical experiments on real data sets illustrate that the proposed method achieves significant improvement over some representative and state-of-the-art unsupervised dimensionality reduction methods.

Zero-shot node classification is a very important challenge for classical semi-supervised node classification algorithms, such as Graph Convolutional Network (GCN) which has been widely applied to node classification. In order to predict the unlabeled nodes from unseen classes, zero-shot node classification needs to transfer knowledge from seen classes to unseen classes. It is crucial to consider the relations between the classes in zero-shot node classification. However, the GCN only considers the relations between the nodes, not the relations between the classes. Therefore, the GCN can not handle the zero-shot node classification effectively. This paper proposes a Dual Bidirectional Graph Convolutional Networks (DBiGCN) that consists of dual BiGCNs from the perspective of the nodes and the classes, respectively. The BiGCN can integrate the relations between the nodes and between the classes simultaneously in an united network. In addition, to make the dual BiGCNs work collaboratively, a label consistency loss is introduced, which can achieve mutual guidance and mutual improvement between the dual BiGCNs. Finally, the experimental results on real-world graph data sets verify the effectiveness of the proposed method.

Classification is one of the most important problems in machine learning and the nature of it is concept cognition. So far, dozens of different classifiers have been designed. Although their working mechanisms vary widely, few of them fully consider concept cognition. In this paper, a new learning machine, fuzzy learning machine (FLM), is proposed from the perspective of concept cognition. Inspired by cognitive science, its working mechanism is of strong interpretability. At the same time, FLM roots in set theory and fuzzy set theory, so FLM has a solid mathematical foundation. The systematic experimental results on a large number of data sets show that FLM can achieve excellent performance, even with the simple implementation.