Due to the power of learning representations from unlabeled graphs, graph contrastive learning (GCL) has shown excellent performance in community detection tasks. Existing GCL-based methods on the community detection usually focused on learning attribute representations of individual nodes, which, however, ignores structure semantics of communities (e.g., nodes in the same community should be structurally cohesive). Therefore, in this paper, we consider the community detection under the community structure semantics and propose an effective framework for graph contrastive learning under structure semantics (GCLS2) to detect communities. To seamlessly integrate interior dense and exterior sparse characteristics of communities with our contrastive learning strategy, we employ classic community structures to extract high-level structural views and design a structure semantic expression module to augment the original structural feature representation. Moreover, we formulate the structure contrastive loss to optimize the feature representation of nodes, which can better capture the topology of communities. To adapt to large-scale networks, we design a high-level graph partitioning (HGP) algorithm that minimizes the community detection loss for GCLS2 online training. It is worth noting that we prove a lower bound on the training of GCLS2 from the perspective of the information theory, explaining why GCLS2 can learn a more accurate representation of the structure. Extensive experiments have been conducted on various real-world graph datasets and confirmed that GCLS2 outperforms nine state-of-the-art methods, in terms of the accuracy, modularity, and efficiency of detecting communities.

Although Federated Fine-Tuning (FFT) facilitates the fine-tuning of Large Language Models (LLMs) across data owners without compromising their privacy, it suffers from severe communication overheads caused by numerous parameters of LLMs even with Parameter-Efficient Fine-Tuning (PEFT) methods. To address this, we propose a novel communication-efficient FFT framework by reducing per-round transmission costs and simultaneously diminishing the required convergence rounds. Specifically, we freeze the parameter-intensive backbone of LLMs, activate low-rank adapters for fine-tuning, and design a parameter quantization mechanism to reduce per-round communication costs. Additionally, inspired by in-context learning, we align the distributions of local data by identifying and rewriting unimportant samples using the global model, retaining important local information, and guiding local fine-tuning towards a uniform trajectory, thereby accelerating convergence and reducing transmission burdens. Experiments on two instruction datasets show that our method can reduce communication costs by up to 7.76× without compromising performance.

Due to the absence or mismatch of semantic information, existing few-shot image generation methods suffer from unsatisfactory generation quality and diversity, which have minimal benefits as data augmentation for downstream classification tasks. Reformatting the contextual and textural information of features at different scales, we propose a novel Feature Equalization Fusion Generative Adversarial Network (EqGAN) for few-shot image generation. Specifically, we first decompose the encoded features into textual and structural components to mitigate the influence of irrelevant and redundant information. Based on feature correlation learning and attention mechanism, we then obtain fused features by refining different contents (i.e., textures and structures) with a more fine-grained semantic alignment. Moreover, an attention-based reconstruction loss and a consistency-based equalization loss are devised to provide better training stability and generation performance. Comprehensive experiments on three public datasets demonstrate that EqGAN not only significantly improves the FID scores (by up to 14.10%) and LPIPS scores (by up to 3.17%) of generated images, but also outperforms the state-of-the-art in terms of accuracy (by up to 3.89%) for downstream classification.

Due to the semantic entanglement in fusion strategies or unstable training in complicated image transformations, existing few-shot image generation methods still suffer from low generation quality and diversity. To tackle the above problems, we propose a novel fusion- and transformation-based framework named content Fusion with style Transformation Generative Adversarial Network (FiTGAN) for few-shot image generation. The basic assumption is that any image consists of a collection of content-related and style-related features. FiTGAN disentangles internal representations with two independent encoders and combines the fused contents and transformed styles to generate new images. Specifically, we design a multi-scale content fusion strategy and a reparameterized style transformation mechanism to learn more fine-grained semantics without changing category-relevant attributes. Furthermore, we formulate a content reconstruction loss and a style divergence loss to provide better training stability and generation performance. Comprehensive experiments on three well-known datasets demonstrate that FiTGAN can not only produce more realistic and diverse images for few-shot image generation but also achieve better classification accuracy for downstream visual applications with limited data.

Although various methods are proposed to generate music accompaniment tracks according to the main music track, they suffer from the problems of modeling music dependencies and representing music data, therefore, generating high-quality music accompaniment remains a challenging task.To address this issue, we propose a multi-track sequential convolutional neural network (MuseCNN) to generate accompaniment tracks corresponding to the main music track.Inspired by the similarity between pianoroll representation and pictures, we transform the pianoroll data into a two-channel music representation matrix and feed it into the convolutional neural network (CNN).Using a hierarchical loss function, we integrate all music tracks to maintain the coherence and harmony of the music.Based on the three-level loss design and multiple CNNs, the problem of modeling music dependencies can be solved.The experimental results indicate that our multi-CNN model can effectively learn complex music dependencies and generate harmonious longsequence polyphonic music.

Few-shot image generation, as an important yet challenging visual task, still suffers from the trade-off between generation quality and diversity. According to the principle of feature-matching learning, existing fusion-based methods usually fuse different features by using similarity measurements or attention mechanisms, which may match features inaccurately and lead to artifacts in the texture and structure of generated images. In this paper, we propose an exact Fusion via Feature Distribution matching Generative Adversarial Network (F2DGAN) for few-shot image generation. The rationale behind this is that feature distribution matching is much more reliable than feature matching to explore the statistical characters in image feature space for limited real-world data. To model feature distributions from only a few examples for feature fusion, we design a novel variational feature distribution matching fusion module to perform exact fusion by empirical cumulative distribution functions. Specifically, we employ a variational autoencoder to transform deep image features into distributions and fuse different features exactly by applying histogram matching. Additionally, we formulate two effective losses to guide the matching process for better fitting our fusion strategy. Extensive experiments compared with state-of-the-art methods on three public datasets demonstrate the superiority of F2DGAN for few-shot image generation in terms of generation quality and diversity, and the effectiveness of data augmentation in downstream classification tasks11Code is available at: https:/github.com/ZYBOBO/F2DGAN.

Due to the increasing popularity of Artificial Intelligence (AI), more and more backdoor attacks are designed to mislead Deep Neural Network (DNN) predictions by manipulating training samples or processes. Although backdoor attacks have been investigated in various scenarios, they still suffer from the problems of both low fidelity of poisoned samples and non-negligible transfer in latent space, which make them easily identified by existing backdoor detection algorithms. To overcome this weakness, this paper proposes a novel frequency-based backdoor attack method named WaveAttack, which obtains high-frequency image features through Discrete Wavelet Transform (DWT) to generate highly stealthy backdoor triggers. By introducing an asymmetric frequency obfuscation method, our approach adds an adaptive residual to the training and inference stages to improve the impact of triggers, thus further enhancing the effectiveness of WaveAttack. Comprehensive experimental results show that, WaveAttack can not only achieve higher effectiveness than state-of-the-art backdoor attack methods, but also outperform them in the fidelity of images (i.e., by up to 28.27\% improvement in PSNR, 1.61\% improvement in SSIM, and 70.59\% reduction in IS). Our code is available at https://github.com/BililiCode/WaveAttack.

Few-shot scene classification aims to develop models that can quickly adapt to new scenes with only a few labeled samples that are not present in training sets. In recent years, convolutional neural networks (CNNs) have made significant advancements in few-shot remote sensing image scene classification tasks. However, most existing approaches focus solely on utilizing high-level embeddings of remote sensing images to learn similarity relations, while neglecting intrinsic hierarchical representations that could be crucial in distinguishing scenes with substantial interclass similarities. To address this limitation, we propose a novel few-shot scene classification method for remote sensing images called hierarchical-relation network (HiReNet). This approach leverages the hierarchical features of a query sample and its corresponding support sample to learn discriminative representations. HiReNet consists of an embedding network and a relation network. The embedding network employs a Siamese architecture to extract representations, while the relation network utilizes these representations for classification. Within the relation network, we introduce a hierarchical relation learning (HRL) structure to capture the hierarchical relations among query and support samples. Additionally, to extract stronger features, we introduce a feature aggregation module that concatenates multilevel features and employs channel attention to re- weight these features. Experimental results demonstrate the superior performance of our HiReNet compared to several state-of-the-art few-shot scene classification methods.

Due to repetitive trial-and-error style interactions between agents and a fixed traffic environment during the policy learning, existing Reinforcement Learning (RL)-based Traffic Signal Control (TSC) methods greatly suffer from long RL training time and poor adaptability of RL agents to other complex traffic environments. To address these problems, we propose a novel Adversarial Inverse Reinforcement Learning (AIRL)-based pre-training method named InitLight, which enables effective initial model generation for TSC agents. Unlike traditional RL-based TSC approaches that train a large number of agents simultaneously for a specific multi-intersection environment, InitLight pre-trains only one single initial model based on multiple single-intersection environments together with their expert trajectories. Since the reward function learned by InitLight can recover ground-truth TSC rewards for different intersections at optimality, the pre-trained agent can be deployed at intersections of any traffic environments as initial models to accelerate subsequent overall global RL training. Comprehensive experimental results show that, the initial model generated by InitLight can not only significantly accelerate the convergence with much fewer episodes, but also own superior generalization ability to accommodate various kinds of complex traffic environments.