Advancements in hardware accelerators, such as graphics processing units and neural processing units, have significantly propelled computer vision research. The vision transformer (ViT), leveraging the multi-head self-attention (MHSA) mechanism, has surpassed convolutional neural networks (CNNs) in accuracy but faces challenges in mobile and edge deployment due to its large size and computational demands. In addition, as privacy concerns push for on-device training, research on quantization methods for ViTs, particularly gradient quantization, has gained attention. Unlike CNNs, ViTs face challenges due to outliers and a complex loss landscape. To address this, we propose a gradient quantization framework that stabilizes training by adapting quantization points based on interquartile ranges and constructing an outlier-robust loss function. Additionally, we employ a scaling method to align quantized gradients with original gradients and adaptively assign the learning rate based on quantization error analysis. When quantizing weights, activations, and gradients to INT8, our method improves performance by 0.52% and 0.21% over DeiT-Base and Swin-Base, respectively, and achieves near parity with MobileViT-S with only a 0.09% accuracy drop. Furthermore, a 2.06x speedup was observed when applying our framework to MobileViT in a CUDA 11.8 environment.
论文检索
输入标题、作者或关键词，从 11,272 篇学术成果中精准定位
WatE: A Wasserstein t-distributed Embedding Method for Information-enriched Graph Visualization
PDF ↗As a fundamental problem of graph analysis, graph visualization aims to embed a set of graphs in a low-dimensional (e.g., 2D) space and provide insights into their distribution and clustering structure. Focusing on this problem, we propose a novel Wasserstein t-distributed embedding (WatE) method, leading to an information-enriched graph visualization paradigm. Our method learns a graph neural network to represent each graph as the mean and covariance of its node embedding distribution. Accordingly, our method can visualize each graph as an ellipse (determined by the mean and the covariance) rather than a single point. The positions of different ellipses reveal the relations among different graphs as traditional visualization methods do, while the size and shape of an ellipse preserve the node-level structural information of the corresponding graph. We propose a regularized t-distributed stochastic neighbor embedding (Rt-SNE) framework to learn the visualization model, deriving a Wasserstein distance-based Student's t-distribution of graph pairs and fitting the distribution to the data distribution under regularization. Both subjective and objective evaluations demonstrate that WatE achieves encouraging performance in various graph visualization and clustering tasks.
Variational autoencoder performs well in community detection on static networks, but it is difficult to directly extend to continuous dynamic networks. The main reason is that traditional methods mainly rely on adjacency structures to complete the inference and generation processes. However, continuous dynamic networks cannot be described by this structure because the inherent timeliness and causality information of the network would be lost. To address this issue, we propose a novel variational autoencoder, CT-VAE, for community detection in continuous dynamic networks, along with its scalable variant, CT-CAVAE. By conceptualizing node interactions as event streams and adopting the Hawkes process to capture temporal dynamics and causality, and incorporating them into the inference process, CT-VAE can effectively extend the traditional inference approach to continuous dynamic networks. Additionally, in the generation phase, CT-VAE combines pseudo-labeling and compact constraint strategies to facilitate the reconstruction process of non-adjacent structures. For the scalable variant, CT-CAVAE, end-to-end community detection is achieved by cleverly combining Gaussian mixture distribution. Extensive experimental results demonstrate that the proposed CT-VAE and CT-CAVAE achieve more favorable performance compared with the state-of-the-art baselines.
Convergence Rate in a Nonlinear Two-Time-Scale Stochastic Approximation with State (Time)-Dependence
PDF ↗The nonlinear two-time-scale stochastic approximation is widely studied under conditions of bounded variances in noise. Motivated by recent advances that allow for variability linked to the current state or time, we consider state- and time-dependent noises. We show that the Lyapunov function exhibits polynomial convergence rates in both cases, with the rate of polynomial delay depending on the parameters of state- or time-dependent noises. Notably, if the state noise parameters fully approach their limiting value, the Lyapunov function achieves an exponential convergence rate. We provide two numerical examples to illustrate our theoretical findings in the context of stochastic gradient descent with Polyak-Ruppert averaging and stochastic bilevel optimization.
Deep neural networks (DNNs) have substantially achieved high predictive accuracy in many vision tasks. However, we find that they are poorly calibrated for crack recognition tasks, as these DNNs tend to produce both under-confident and over-confident predictions in such safety-critical applications, thereby limiting their practical use in real-world scenarios. To address this issue, we propose a novel attack-inspired calibration loss (AICL) that explicitly regularizes class probabilities to be better confidence estimation. Specifically, we first propose the attack-inspired correctness estimation method (ACE) that aims to estimate the correctness degree of each sample via adversarial attacks. Then, we propose Correctness-aware Distribution Guidance, which starts from a distribution perspective that enforces the ordinal ranking of the predicted confidence referring to the estimated correctness degree. The proposed method can be conveniently implemented on top of any DNNs-based crack recognition model by serving as a plug-and-play loss function. To address the limited availability of related benchmarks, we collect a fully annotated dataset, namely, Bridge2024, which involves inconsistent cracks and noisy backgrounds in real-world bridges. Our AICL outperforms the state-of-art calibration methods on various benchmark datasets including CRACK2019, SDNET2018, and our BRIDGE2024.
The rapid development of diffusion models has significantly advanced AI-generated content (AIGC), particularly in Text-to-Image (T2I) and Text-to-Video (T2V) generation. Text-based video editing, leveraging these generative capabilities, has emerged as a promising field, enabling precise modifications to videos based on text prompts. Despite the proliferation of innovative video editing models, there is a conspicuous lack of comprehensive evaluation benchmarks that holistically assess these models’ performance across various dimensions. Existing evaluations are limited and inconsistent, typically summarizing overall performance with a single score, which obscures models’ effectiveness on individual editing tasks. To address this gap, we propose EditBoard, the first comprehensive evaluation benchmark for text-based video editing models. EditBoard encompasses nine automatic metrics across four dimensions, evaluating models on four task categories and introducing three new metrics to assess fidelity. This task-oriented benchmark facilitates objective evaluation by detailing model performance and providing insights into each model’s strengths and weaknesses. By open-sourcing EditBoard, we aim to standardize evaluation and advance the development of robust video editing models.
Contrastive clustering performs clustering and data representation in a unified model, where instance- and cluster-level constrastive learning are conducted simultaneously. However, commonly-used data augmentation methods make contrastive mechanism effect but may cause representation learning getting stuck in domain-specific information, which further deteriorates clustering performance and limits generalization ability. To this end, we propose a new framework, named Generalized Contrastive Clustering with domain shifts modeling (GeCC), which can integrate diverse domain knowledge to improve the clustering performance. Specifically, we first design a cluster-guided domain shifts modeling module to synthesize a reference view with diverse domain information. Then, we introduce instance representation and cluster assignment contrastive modules with well-designed attention weights to guide the representation learning and clustering. In this way, our method can maximize the extraction of cluster-related information and avoid over-fitting domain-specific features. Experimental results on four benchmark datasets demonstrate that our proposed method consistently outperforms other state-of-the-art methods.
A Pioneering Neural Network Method for Efficient and Robust Fuel Sloshing Simulation in Aircraft
PDF ↗Simulating fuel sloshing within aircraft tanks during flight is crucial for aircraft safety research. Traditional methods based on Navier-Stokes equations are computationally expensive. In this paper, we treat fluid motion as point cloud transformation and propose the first neural network method specifically designed for simulating fuel sloshing in aircraft. This model is also the first deep learning model capable of stably modeling fluid particle dynamics in such complex scenarios. Our triangle feature fusion design achieves an optimal balance among fluid dynamics modeling, momentum conservation constraints, and global stability control. Additionally, we constructed the Fueltank dataset, the first dataset for aircraft fuel surface sloshing. It comprises 320,000 frames across four typical tank types and covers a wide range of flight maneuvers, including multi-directional rotations. We conducted comprehensive experiments on both our dataset and the take-off scenario of the aircraft. Compared to existing neural network-based fluid simulation algorithms, we significantly enhanced accuracy while maintaining high computational speed. Compared to traditional SPH methods, our speed improved approximately 10 times. Furthermore, compared to traditional fluid simulation software such as Flow3D, our computation speed increased by more than 300 times.
Graph contrastive learning (GCL) has drawn much research attention for its ability to learn node representations in a self-supervised manner. However, the homophily assumption inherent in GNN encoders limits the direction (macro-level) and the process (micro-level) of message passing in current GCL frameworks, impairing the expressive power of GCL in non-homophilous graphs. This paper presents a novel framework that employs Macro and Micro Message Passing in GCL (M3P-GCL) to overcome these limitations and advance performance in both homophilous and non-homophilous graphs. Specifically, at the macro-level, we integrate structural and attribute views to enhance the direction of message passing, and employ an Aligned Priority-Supporting View Encoding (APS-VE) strategy to facilitate contrastive training; at the micro-level, we propose an Adaptive Self-Propagation (ASP) strategy based on role segmentation of self-loops to diversify the process of message passing in the encoder. These enhancements effectively address the limitations imposed by the homophily assumption. Experiments demonstrate that M3P-GCL outperforms both supervised and unsupervised baselines in the node classification task on various datasets with different levels of homophily.
Anchor-based multi-view clustering has received extensive attention due to its efficient performance. Existing methods only focus on how to dynamically learn anchors from the original data and simultaneously construct anchor graphs describing the relationships between samples and perform clustering, while ignoring the reality of anchors, i.e., high-quality anchors should be generated uniformly from different clusters of data rather than scattered outside the clusters. To deal with this problem, we propose a noval method termed Anchor Learning with Potential Cluster Constraints for Multi-view Clustering (ALPC) method. Specifically, ALPC first establishes a shared latent semantic module to constrain anchors to be generated from specific clusters, and subsequently, ALPC improves the representativeness and discriminability of anchors by adapting the anchor graph to capture the common clustering center of mass from samples and anchors, respectively. Finally, ALPC combines anchor learning and graph construction into a unified framework for collaborative learning and mutual optimization to improve the clustering performance. Extensive experiments demonstrate the effectiveness of our proposed method compared to some state-of-the-art MVC methods.
Recently, deep Multi-Agent Reinforcement Learning (MARL) has demonstrated its potential to tackle complex cooperative tasks, pushing the boundaries of AI in collaborative environments. However, the efficiency of these systems is often compromised by inadequate sample utilization and a lack of diversity in learning strategies. To enhance MARL performance, we introduce a novel sample reuse approach that dynamically adjusts policy updates based on observation novelty. Specifically, we employ a Random Network Distillation (RND) network to gauge the novelty of each agent's current state, assigning additional sample update opportunities based on the uniqueness of the data. We name our method Multi-Agent Novelty-GuidEd sample Reuse (MANGER). This method increases sample efficiency while promoting exploration and diverse agent behaviors. Our evaluations confirm substantial improvements in MARL effectiveness in complex cooperative scenarios such as Google Research Football and super-hard StarCraft II micromanagement tasks.
Network traffic includes data transmitted across a network, such as web browsing and file transfers, and is organized into packets (small units of data) and flows (sequences of packets exchanged between two endpoints). Classifying encrypted traffic is essential for detecting security threats and optimizing network management. Recent advancements have highlighted the superiority of foundation models in this task, particularly for their ability to leverage large amounts of unlabeled data and demonstrate strong generalization to unseen data. However, existing methods that focus on token-level relationships fail to capture broader flow patterns, as tokens, defined as sequences of hexadecimal digits, typically carry limited semantic information in encrypted traffic. These flow patterns, which are crucial for traffic classification, arise from the interactions between packets within a flow, not just their internal structure. To address this limitation, we propose a Multi-Instance Encrypted Traffic Transformer (MIETT), which adopts a multi-instance approach where each packet is treated as a distinct instance within a larger bag representing the entire flow. This enables the model to capture both token-level and packet-level relationships more effectively through Two-Level Attention (TLA) layers, improving the model's ability to learn complex packet dynamics and flow patterns. We further enhance the model's understanding of temporal and flow-specific dynamics by introducing two novel pre-training tasks: Packet Relative Position Prediction (PRPP) and Flow Contrastive Learning (FCL). After fine-tuning, MIETT achieves state-of-the-art (SOTA) performance across five datasets, demonstrating its effectiveness in classifying encrypted traffic and understanding complex network behaviors.
Sequence Complementor: Complementing Transformers for Time Series Forecasting with Learnable Sequences
PDF ↗Since its introduction, the transformer has shifted the development trajectory away from traditional models (e.g., RNN, MLP) in time series forecasting, which is attributed to its ability to capture global dependencies within temporal tokens. Follow-up studies have largely involved altering the tokenization and self-attention modules to better adapt Transformers for addressing special challenges like non-stationarity, channel-wise dependency, and variable correlation in time series. However, we found that the expressive capability of sequence representation is a key factor influencing Transformer performance in time forecasting after investigating several representative methods, where there is an almost linear relationship between sequence representation entropy and mean square error, with more diverse representations performing better. In this paper, we propose a novel attention mechanism with Sequence Complementors and prove feasible from an information theory perspective, where these learnable sequences are able to provide complementary information beyond current input to feed attention. We further enhance the Sequence Complementors via a diversification loss that is theoretically covered. The empirical evaluation of both long-term and short-term forecasting has confirmed its superiority over the recent state-of-the-art methods.
Despite some promising results in federated learning using game-theoretical methods, most existing studies mainly employ a one-level game in either a cooperative or competitive environment, failing to capture the complex dynamics among participants in practice. To address this issue, we propose DualGFL, a novel federated learning framework with a dual-level game in cooperative-competitive environments. DualGFL includes a lower-level hedonic game where clients form coalitions and an upper-level multi-attribute auction game where coalitions bid for training participation. At the lower-level DualGFL, we introduce a new auction-aware utility function and propose a Pareto-optimal partitioning algorithm to find a Pareto-optimal partition based on clients' preference profiles. At the upper-level DualGFL, we formulate a multi-attribute auction game with resource constraints and derive equilibrium bids to maximize coalitions' winning probabilities and profits. A greedy algorithm is proposed to maximize the utility of the central server. Extensive experiments on real-world datasets demonstrate DualGFL's effectiveness in improving both server utility and client utility.
The reward signal plays a central role in defining the desired behaviors of agents in reinforcement learning (RL). Rewards collected from realistic environments could be perturbed, corrupted, or noisy due to an adversary, sensor error, or because they come from subjective human feedback. Thus, it is important to construct agents that can learn under such rewards. Existing methodologies for this problem make strong assumptions, including that the perturbation is known in advance, clean rewards are accessible, or that the perturbation preserves the optimal policy. We study a new, more general, class of unknown perturbations, and introduce a distributional reward critic framework for estimating reward distributions and perturbations during training. Our proposed methods are compatible with any RL algorithm. Despite their increased generality, we show that they achieve comparable or better rewards than existing methods in a variety of environments, including those with clean rewards. Under the challenging and generalized perturbations we study, we win/tie the highest return in 44/48 tested settings (compared to 11/48 for the best baseline). Our results broaden and deepen our ability to perform RL in reward-perturbed environments.
The strategy of selecting ``most informative'' hard samples in active learning has proven a boon for alleviating the challenges of few-shot learning and costly data annotation in deep learning. However, this very preference towards hard samples engenders bias issues, thereby impeding the full potential of active learning. It has witnessed an increasing trend to mitigate this stubborn problem, yet most neglect the quantification of bias itself and the direct rectification of dynamically evolving biases. Revisiting the bias issue, this paper presents an active learning approach based on the Variational Gradient Rectifier (VaGeRy). First, we employ variational methods to quantify bias at the level of latent state representations. Then, harnessing historical training dynamics, we introduce Uncertainty Consistency Regularization and Fluctuation Restriction, which asynchronously iterate to rectify gradient backpropagation. Extensive experiments demonstrate that our proposed methodology effectively counteracts bias phenomena in a majority of active learning scenarios
Smoothness Really Matters: A Simple Yet Effective Approach for Unsupervised Graph Domain Adaptation
PDF ↗Unsupervised Graph Domain Adaptation (UGDA) seeks to bridge distribution shifts between domains by transferring knowledge from labeled source graphs to given unlabeled target graphs. Existing UGDA methods primarily focus on aligning features in the latent space learned by graph neural networks (GNNs) across domains, often overlooking structural shifts, resulting in limited effectiveness when addressing structurally complex transfer scenarios. Given the sensitivity of GNNs to local structural features, even slight discrepancies between source and target graphs could lead to significant shifts in node embeddings, thereby reducing the effectiveness of knowledge transfer. To address this issue, we introduce a novel approach for UGDA called Target-Domain Structural Smoothing (TDSS). TDSS is a simple and effective method designed to perform structural smoothing directly on the target graph, thereby mitigating structural distribution shifts and ensuring the consistency of node representations. Specifically, by integrating smoothing techniques with neighbor- hood sampling, TDSS maintains the structural coherence of the target graph while mitigating the risk of over-smoothing. Our theoretical analysis shows that TDSS effectively reduces target risk by improving model smoothness. Empirical results on three real-world datasets demonstrate that TDSS outperforms recent state-of-the-art baselines, achieving significant improvements across six transfer scenarios.
In the realm of class-incremental learning (CIL), alleviating the catastrophic forgetting problem is a pivotal challenge. This paper discovers a counter-intuitive observation: by incorporating domain shift into CIL tasks, the forgetting rate is significantly reduced. Our comprehensive studies demonstrate that incorporating domain shift leads to a clearer separation in the feature distribution across tasks and helps reduce parameter interference during the learning process. Inspired by this observation, we propose a simple yet effective method named DisCo to deal with CIL tasks. DisCo introduces a lightweight prototype pool that utilizes contrastive learning to promote distinct feature distributions for the current task relative to previous ones, effectively mitigating interference across tasks. DisCo can be easily integrated into existing state-of-the-art class-incremental learning methods. Experimental results show that incorporating our method into various CIL methods achieves substantial performance improvements, validating the benefits of our approach in enhancing class-incremental learning by separating feature representation and reducing interference. These findings illustrate that DisCo can serve as a robust fashion for future research in class-incremental learning.
The learnware paradigm aims to establish a learnware dock system of numerous well-trained machine learning models, enabling users to reuse existing helpful models for their tasks instead of starting from scratch. Each learnware in the system is a well-established model submitted by its developer, associated with a specification generated by the learnware dock system. The specification characterizes the specialty of the corresponding model, enabling it to be identified accurately for new task requirements. Existing specification generation methods are mostly based on the Reduced Kernel Mean Embedding (RKME) technique, which uses the Maximum Mean Discrepancy (MMD) in the Reproducing Kernel Hilbert Space (RKHS) to seek a reduced set that characterizes the model's capabilities. However, existing RKME-based methods mainly utilize feature information to generate specifications by assuming the existence of the ground-truth labeling function, while leaving the label information, which is capable of providing rich semantic characterization, untouched. Furthermore, the quality of the generated specifications heavily relies on the choice of the kernels, which makes it prohibitive to adapt to all real-world scenarios. In this paper, to overcome the above limitations, we propose a novel specification approach named LANE, i.e., Label-Aware Neural Embedding. In LANE, the neural embedding space is utilized to replace the RKHS, effectively circumventing the step of kernel selection and thereby addressing the dependency on kernels in existing RKME-based specification methods. More importantly, LANE uses the label information as additional supervision to enhance the generation process, resulting in specifications of superior quality. Extensive experiments demonstrate the effectiveness and superiority of the proposed LANE approach in the learnware paradigm.
SpotDiff: Spatial Gene Expression Imputation Diffusion with Single-Cell RNA Sequencing Data Integration
PDF ↗The advent of Spatial Transcriptomics (ST) has revolutionized understanding of tissue architecture by creating high-resolution maps of gene expression patterns. However, the low capture rate of ST leads to significant sparsity. The aim of imputation is to recover biological signals by imputing the dropouts in ST data to approximate the true expression values. In this paper, we introduce a Spatial Gene Expression Imputation Diffusion model to facilitate ST data imputation, and our model is referred to as SpotDiff. Specifically, we incorporate a spot-gene prompt learning module to capture the association between spots and genes. Further, SpotDiff integrates single-cell RNA sequencing data to impute gene expression at each spot. The proposed approach is able to reduce the uncertainty in the imputation process, since the aggregation of multiple single-cell measurements yield a stable representation of the corresponding spot expression profile. Extensive experiments have been performed to demonstrate that SpotDiff outperforms existing imputation methods across multiple benchmarks in terms of yielding more accurate and biologically relevant gene expression profiles, particularly in highly sparse scenarios.