Hashing methods have recently attracted extensive attention in cross-modal retrieval. Most supervised hashing methods attempt to preserve the semantic information into hash codes by leveraging the original logical label matrix. However, they generally treat all labels equally, and ignore the relative significance of different labels due to the variety of data features. In this article, we argue that exploring the relative importance of labels benefits the enhancement of semantic information, and we propose a novel LAbel Distribution Guided Hashing (LADH) method for cross-modal retrieval. In particular, LADH first learns a feature-induced label distribution for each sample to weigh different labels, which leverages the multi-modal feature information to enrich the semantic label information. By jointly using the learned label distributions and multi-modal features, the latent representation and hash codes are obtained with multi-modal feature selection and enhanced semantic similarities embedded. An efficient algorithm is designed to solve the proposed method whose time complexity is linear to the number of the training instances. Experimental results on several public benchmark datasets verify the effectiveness and efficiency of our method compared with the state-of-the-art methods.

With the capacity to capture high-order collaborative signals, Graph Neural Networks (GNNs) have emerged as powerful methods in Recommender Systems (RS). However, their efficacy often hinges on the assumption that training and testing data share the same distribution (\aka IID assumption), and exhibits significant declines under distribution shifts. Distribution shifts commonly arises in RS, often attributed to the dynamic nature of user preferences or ubiquitous biases during data collection in RS. Despite its significance, researches on GNN-based recommendation against distribution shift are still sparse. To bridge this gap, we propose Distributionally Robust GNN (DR-GNN) that incorporates Distributional Robust Optimization (DRO) into the GNN-based recommendation. DR-GNN addresses two core challenges: 1) To enable DRO to cater to graph data intertwined with GNN, we reinterpret GNN as a graph smoothing regularizer, thereby facilitating the nuanced application of DRO; 2) Given the typically sparse nature of recommendation data, which might impede robust optimization, we introduce slight perturbations in the training distribution to expand its support. Notably, while DR-GNN involves complex optimization, it can be implemented easily and efficiently. Our extensive experiments validate the effectiveness of DR-GNN against three typical distribution shifts. The code is available at https://github.com/WANGBohaO-jpg/DR-GNN.

Semi-supervised learning (SSL) aims to reduce the heavy reliance of current deep models on costly manual annotation by leveraging a large amount of unlabeled data in combination with a much smaller set of labeled data. However, most existing SSL methods assume that all labeled and unlabeled data are drawn from the same feature distribution, which can be impractical in real-world applications. In this study, we take the initial step to systematically investigate the open-domain semi-supervised learning setting, where a feature distribution mismatch exists between labeled and unlabeled data. In pursuit of an effective solution for open-domain SSL, we propose a novel framework calledGlocalMatch, which aims to exploit bothglobal and local(i.e., glocal) cluster structure of open-domain unlabeled data. The glocal cluster structure is utilized in two complementary ways. First, GlocalMatch optimizes a Glocal Cluster Compacting (GCC) objective, that encourages feature representations of the same class, whether with in the same domain or across different domains, to become closer to each other. Second, GlocalMatch incorporates a Glocal Semantic Aggregation (GSA) strategy to produce more reliable pseudo-labels by aggregating predictions from neighboring clusters. Extensive experiments demonstrate that GlocalMatch outperforms the state-of-the-art SSL methods significantly, achieving superior performance for both in-domain and out-of-domain generalization.

Domain Generalization (DG) aims to generalize to arbitrary unseen domains. A promising approach to improve model generalization in DG is the identification of flat minima. One typical method for this task is SWAD, which involves averaging weights along the training trajectory. However, the success of weight averaging depends on the diversity of weights, which is limited when training with a small learning rate. Instead, we observe that leveraging a large learning rate can simultaneously promote weight diversity and facilitate the identification of flat regions in the loss landscape. However, employing a large learning rate suffers from the convergence problem, which cannot be resolved by simply averaging the training weights. To address this issue, we introduce a training strategy called Lookahead which involves the weight interpolation, instead of average, between fast and slow weights. The fast weight explores the weight space with a large learning rate, which is not converged while the slow weight interpolates with it to ensure the convergence. Besides, weight interpolation also helps identify flat minima by implicitly optimizing the local entropy loss that measures flatness. To further prevent overfitting during training, we propose two variants to regularize the training weight with weighted averaged weight or with accumulated history weight. Taking advantage of this new perspective, our methods achieve state-of-the-art performance on both classification and semantic segmentation domain generalization benchmarks. The code is available athttps://github.com/koncle/DG-with-Large-LR.

Anomaly detection of time series is vital in various scenarios with explosively growing time series data. However, the non-stationary time series degrade the performance of current anomaly detection methods, where data drift causes unpredictable changes. This paper proposes a Drift-aware Anomaly Detection (DAD) method for detecting anomalies in non-stationary time series. DAD adopts a self-attention mechanism to learn an embedding, distinguishing the anomaly embeddings from the normal embeddings. Next, the KL divergence calculates the drift deviation between two data segments at adjacent periods. Then, the drift deviation module combined with the latent vector which is used to reconstruct the original vector. During the encoding stage of the time series, the latent code is modeled using different Gaussian mixture distributions and the data reconstruction error at each time tick is regarded as an anomaly metric. Furthermore, we propose a new metric to measure the degree of drift deviation for a dataset used for a fair experiment comparison. Experimental results on several public datasets and a newly collected sensor dataset demonstrate that for the non-stationary time series anomaly detection task, DAD outperforms state-of-the-art anomaly detection models up to 11.5% on the F1score.

Hashing methods have captured much attention for cross-modal retrieval in recent years. Most existing approaches mainly focus on preserving the semantic similarity across heterogeneous modalities in a shared Hamming subspace, while the label information and potential correlations of multi-label semantics are not fully excavated. In this article, a novel Adaptive Label correlation based asymmEtric Cross-modal Hashing method, i.e., ALECH, is proposed for cross-modal retrieval. ALECH decomposes hash learning into two steps, hash codes learning and hash functions learning. For hash codes learning, the high-order semantic label correlations are adaptively exploited to guide the latent feature learning, while simultaneously generating the binary codes in a discrete manner. The asymmetric strategy is utilized to connect the latent feature space and Hamming space, and preserve the pairwise semantic similarity. Different from other two-step methods that directly adopt simple least-squares regression to learn hash functions based on binary codes, ALECH leverages both hash codes and semantic labels for hash functions learning which further preserves the similarity. Experiments on several benchmark datasets demonstrate that the proposed ALECH method outperforms the state-of-the-art cross-hashing methods.

Owing to its query and storage efficiency, hash learning has sparked much interest for Cross-Modal Retrieval (CMR) task. Previous literatures have proved the superiority of supervised Cross-Modal Hashing (CMH) methods over unsupervised ones. Nevertheless, most existing supervised CMH methods still suffer from some limitations: 1) it is assumed that the observed labels of training data are complete and accurate, which may be impractical due to the missing and wrong class assignments in real applications, and 2) the semantic information is not fully excavated, especially for the semantic correlations among labels. To address these issues, this paper proposes a Weakly-supervised enhAnced Semantic-aware Hashing (WASH) method which simultaneously estimates the label noises and performs enhanced semantic-aware hash learning. WASH employs the low-rank and sparse decomposition to alleviate the label noises, and a high-level semantic factor as well as a semantic correlation matrix is obtained by low-rank factorization on the noise-reduced labels. The low-rank semantic factors and multi-modal features are jointly factorized into a common subspace to reduce the heterogeneity gaps, so as to enhance the semantic awareness of shared representation. In this way, the hash codes can be obtained by binarizing the shared representation with pairwise semantic similarity preserved. Experiments on several benchmark datasets verify the effectiveness of the proposed method in comparison with the state-of-the-art CMH approaches.

The Alternating Direction Method of Multipliers (ADMM) has been developed recently for distributed classification. Nevertheless, the widely-existing class imbalance problem has not been well investigated. Furthermore, previous imbalanced classification methods lack of efforts in studying the complex imbalance problem in a distributed environment. In this paper, we consider the imbalance problem as distributed data imbalance which includes three imbalance issues: (i) within-node class imbalance, (ii)between-node class imbalance, and (iii) between-node structure imbalance. In order to adequately deal with imbalanced data as well as improve time efficiency, a novel distributed Cost-Sensitive classification algorithm via Group-based ADMM (CS-GADMM) is proposed. Briefly, CS-GADMM derives the classification problem as a series of sub-problems with within-node class imbalance. To alleviate the time delay caused by between-node class imbalance, we propose a extension of dual coordinate descent method for the sub-problem optimization. Meanwhile, for between-node structure imbalance, we discreetly study the relationship between local functions, and combine the resulting local variables intra-group to update the global variables for prediction. The experimental results on various imbalanced datasets validate that CS-GADMM could be a efficient algorithm for imbalanced classification.