点云完成任务旨在预测不完整的点云的缺失部分,并通过详细信息生成完整的点云。在本文中,我们提出了一个新颖的点云完成网络,即完成。具体而言,从具有不同分辨率的点云中学到了特征,该分辨率是从不完整输入中采样的,并根据几何结构转换为一系列\ textit {spots}。然后,提出了基于变压器的密集关系增强模块(DRA),以学习\ textit {spots}中的特征,并考虑这些\ textit {spots}之间的相关性。 DRA由点局部注意模块(PLA)和点密集的多尺度注意模块(PDMA)组成,其中PLA通过适应邻居的权重,PDMA Expolo the Local \ textit {spots}捕获本地信息。这些\ textit {spots}之间的全局关系以多尺度的密集连接方式。最后,由\ textit {spots}通过多分辨率点融合模块(MPF)预测完整形状,该模块(mpf)逐渐从\ textit {spots}中逐渐生成完整的点云,并基于这些生成的点进行更新\ textit {spots}云。实验结果表明,由于基于变压器的DRA可以从不完整的输入中学习表达性特征,并且MPF可以完全探索这些功能以预测完整的输入,因此我们的方法在很大程度上优于先进方法。
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深入学习云越来越发展。将点与其邻居分组并对它们进行卷积相同的操作可以了解点云的本地特征,但此方法薄弱以提取长距离全局功能。在整个点云上执行关注的变换器可以有效地学习它的全局特征,但此方法几乎不会提取本地详细功能。在本文中,我们提出了一种新颖的模块,可以同时提取和保险熔断本地和全局功能,该功能被命名为CT-Block。 CT-块由两个分支组成,其中字母C表示卷积分支,字母T表示变压器分支。卷积分支对分组邻点的卷积进行了卷积以提取本地功能。同时,变压器分支对整个点云执行偏移注意过程以提取全局功能。通过CT-块中的特征传输元件构造的桥梁,本地和全局特征在学习期间彼此引导并有效地融合。我们应用CT-Block构建点云分类和分段网络,并评估几个公共数据集的性能。实验结果表明,由于CT-Block学习的特征是多种表现力的,所以由CT-Block构成的网络的性能在点云分类和分割任务实现现有技术。
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后门攻击对NLP模型构成了新的威胁。在后门攻击中构建中毒数据的标准策略是将触发器(例如,稀有字)插入所选句子,并将原始标签更改为目标标签。该策略具有从触发器和标签视角轻松检测到的严重缺陷:注入的触发器,通常是一种罕见的单词,导致异常的自然语言表达,因此可以通过防御模型容易地检测到异常的自然语言表达;改变的目标标签会导致误报标记的示例,因此可以通过手动检查容易地检测到。要处理此问题,请在本文中,我们提出了一种新的策略来执行不需要外部触发的文本后门攻击,并且中毒样品被正确标记。拟议策略的核心思想是构建清洁标记的例子,其标签是正确的,但可以导致测试标签在与培训集合融合时的变化。为了产生中毒清洁标记的例子,我们提出了一种基于遗传算法的句子生成模型,以满足文本数据的不可微差特性。广泛的实验表明,拟议的攻击策略不仅有效,而且更重要的是,由于其令人触发和清洁的性质,难以防御。我们的工作标志着在NLP中开发令人触发的攻击策略的第一步。
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Blind image quality assessment (BIQA) remains challenging due to the diversity of distortion and image content variation, which complicate the distortion patterns crossing different scales and aggravate the difficulty of the regression problem for BIQA. However, existing BIQA methods often fail to consider multi-scale distortion patterns and image content, and little research has been done on learning strategies to make the regression model produce better performance. In this paper, we propose a simple yet effective Progressive Multi-Task Image Quality Assessment (PMT-IQA) model, which contains a multi-scale feature extraction module (MS) and a progressive multi-task learning module (PMT), to help the model learn complex distortion patterns and better optimize the regression issue to align with the law of human learning process from easy to hard. To verify the effectiveness of the proposed PMT-IQA model, we conduct experiments on four widely used public datasets, and the experimental results indicate that the performance of PMT-IQA is superior to the comparison approaches, and both MS and PMT modules improve the model's performance.
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It has been observed in practice that applying pruning-at-initialization methods to neural networks and training the sparsified networks can not only retain the testing performance of the original dense models, but also sometimes even slightly boost the generalization performance. Theoretical understanding for such experimental observations are yet to be developed. This work makes the first attempt to study how different pruning fractions affect the model's gradient descent dynamics and generalization. Specifically, this work considers a classification task for overparameterized two-layer neural networks, where the network is randomly pruned according to different rates at the initialization. It is shown that as long as the pruning fraction is below a certain threshold, gradient descent can drive the training loss toward zero and the network exhibits good generalization performance. More surprisingly, the generalization bound gets better as the pruning fraction gets larger. To complement this positive result, this work further shows a negative result: there exists a large pruning fraction such that while gradient descent is still able to drive the training loss toward zero (by memorizing noise), the generalization performance is no better than random guessing. This further suggests that pruning can change the feature learning process, which leads to the performance drop of the pruned neural network. Up to our knowledge, this is the \textbf{first} generalization result for pruned neural networks, suggesting that pruning can improve the neural network's generalization.
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Time-series anomaly detection is an important task and has been widely applied in the industry. Since manual data annotation is expensive and inefficient, most applications adopt unsupervised anomaly detection methods, but the results are usually sub-optimal and unsatisfactory to end customers. Weak supervision is a promising paradigm for obtaining considerable labels in a low-cost way, which enables the customers to label data by writing heuristic rules rather than annotating each instance individually. However, in the time-series domain, it is hard for people to write reasonable labeling functions as the time-series data is numerically continuous and difficult to be understood. In this paper, we propose a Label-Efficient Interactive Time-Series Anomaly Detection (LEIAD) system, which enables a user to improve the results of unsupervised anomaly detection by performing only a small amount of interactions with the system. To achieve this goal, the system integrates weak supervision and active learning collaboratively while generating labeling functions automatically using only a few labeled data. All of these techniques are complementary and can promote each other in a reinforced manner. We conduct experiments on three time-series anomaly detection datasets, demonstrating that the proposed system is superior to existing solutions in both weak supervision and active learning areas. Also, the system has been tested in a real scenario in industry to show its practicality.
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As an important variant of entity alignment (EA), multi-modal entity alignment (MMEA) aims to discover identical entities across different knowledge graphs (KGs) with multiple modalities like images. However, current MMEA algorithms all adopt KG-level modality fusion strategies but ignore modality differences among individual entities, hurting the robustness to potential noise involved in modalities (e.g., unidentifiable images and relations). In this paper we present MEAformer, a multi-modal entity alignment transformer approach for meta modality hybrid, to dynamically predict the mutual correlation coefficients among modalities for instance-level feature fusion. A modal-aware hard entity replay strategy is also proposed for addressing vague entity details. Extensive experimental results show that our model not only achieves SOTA performance on multiple training scenarios including supervised, unsupervised, iterative, and low resource, but also has limited parameters, optimistic speed, and good interpretability. Our code will be available soon.
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The task of video prediction and generation is known to be notoriously difficult, with the research in this area largely limited to short-term predictions. Though plagued with noise and stochasticity, videos consist of features that are organised in a spatiotemporal hierarchy, different features possessing different temporal dynamics. In this paper, we introduce Dynamic Latent Hierarchy (DLH) -- a deep hierarchical latent model that represents videos as a hierarchy of latent states that evolve over separate and fluid timescales. Each latent state is a mixture distribution with two components, representing the immediate past and the predicted future, causing the model to learn transitions only between sufficiently dissimilar states, while clustering temporally persistent states closer together. Using this unique property, DLH naturally discovers the spatiotemporal structure of a dataset and learns disentangled representations across its hierarchy. We hypothesise that this simplifies the task of modeling temporal dynamics of a video, improves the learning of long-term dependencies, and reduces error accumulation. As evidence, we demonstrate that DLH outperforms state-of-the-art benchmarks in video prediction, is able to better represent stochasticity, as well as to dynamically adjust its hierarchical and temporal structure. Our paper shows, among other things, how progress in representation learning can translate into progress in prediction tasks.
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Implicit regularization is an important way to interpret neural networks. Recent theory starts to explain implicit regularization with the model of deep matrix factorization (DMF) and analyze the trajectory of discrete gradient dynamics in the optimization process. These discrete gradient dynamics are relatively small but not infinitesimal, thus fitting well with the practical implementation of neural networks. Currently, discrete gradient dynamics analysis has been successfully applied to shallow networks but encounters the difficulty of complex computation for deep networks. In this work, we introduce another discrete gradient dynamics approach to explain implicit regularization, i.e. landscape analysis. It mainly focuses on gradient regions, such as saddle points and local minima. We theoretically establish the connection between saddle point escaping (SPE) stages and the matrix rank in DMF. We prove that, for a rank-R matrix reconstruction, DMF will converge to a second-order critical point after R stages of SPE. This conclusion is further experimentally verified on a low-rank matrix reconstruction problem. This work provides a new theory to analyze implicit regularization in deep learning.
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Gradient-based explanation is the cornerstone of explainable deep networks, but it has been shown to be vulnerable to adversarial attacks. However, existing works measure the explanation robustness based on $\ell_p$-norm, which can be counter-intuitive to humans, who only pay attention to the top few salient features. We propose explanation ranking thickness as a more suitable explanation robustness metric. We then present a new practical adversarial attacking goal for manipulating explanation rankings. To mitigate the ranking-based attacks while maintaining computational feasibility, we derive surrogate bounds of the thickness that involve expensive sampling and integration. We use a multi-objective approach to analyze the convergence of a gradient-based attack to confirm that the explanation robustness can be measured by the thickness metric. We conduct experiments on various network architectures and diverse datasets to prove the superiority of the proposed methods, while the widely accepted Hessian-based curvature smoothing approaches are not as robust as our method.
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