无监督的终身学习是指随着时间的流逝学习的能力，同时在没有监督的情况下记住以前的模式。以前的作品假设了有关传入数据（例如，了解类边界）的强大先验知识，这些数据是在复杂且不可预测的环境中无法获得的。在本文中，以现实世界情景的启发，我们通过类外的流媒体数据正式定义了在线无监督的终身学习问题，该数据是非IID和单次通道。由于缺乏标签和先验知识，该问题比现有的终身学习问题更具挑战性。为了解决这个问题，我们提出了自我监督的对比终身学习（比例），该学习提取并记住了知识。规模围绕三个主要组成部分进行设计：伪监督的对比损失，自我监督的遗忘损失以及统一子集选择的在线记忆更新。这三个组件旨在协作以最大程度地提高学习表现。我们的损失功能利用成对相似性，因此消除了对监督或先验知识的依赖。我们在IID和四个非IID数据流下进行了全面的规模实验。在所有设置上，缩放量优于最佳最新算法，在CIFAR-10，CIFAR-100和Subimagenet数据集上，提高了高达6.43％，5.23％和5.86％的KNN精度。

本文研究基于单眼图像的类别级对象构成估计。姿势感知的生成模型的最新进展为解决这一具有挑战性的任务使用分析铺平了道路。这个想法是依次更新生成模型的一组潜在变量，例如，姿势，形状和外观，直到生成的图像最能与观察结果一致为止。但是，收敛和效率是该推理程序的两个挑战。在本文中，我们从视觉导航的角度更深入地研究了分析的推断，并研究了该特定任务的良好导航策略。我们通过在收敛，鲁棒性和效率方面进行彻底比较，评估三种不同的策略，包括梯度下降，增强学习和模仿学习。此外，我们表明一种简单的混合方法会导致有效而有效的解决方案。我们进一步将这些策略与最先进的方法进行了比较，并在利用现成的姿势感知生成模型的合成和现实数据集上展示了卓越的性能。

双曲线空间可以嵌入树度量，其失真几乎没有失真，是用于建模实际数据和语义的层次结构的理想性质。虽然高维嵌入式通常会导致更好的表示，但由于非琐碎的优化以及高维双曲数据缺乏可视化，大多数双曲模型利用低维嵌入式。我们提出了CO-SNE，将欧几里德空间可视化工具T-SNE延伸到双曲线空间。像T-SNE一样，它将数据点之间的距离转换为联合概率，并尝试最小化高维数据的联合概率之间的kullback-leibler分歧$ x $和低维嵌入$ y $。然而，与欧几里德空间不同，双曲线空间不均匀：体积可能在远离原点的位置包含更多点。因此，CO-SNE为$ x $和hyberbolic \ Underline {c} auchy而不是t-sne的学生的t分布，而不是$ y $，它还试图将$ x $的单个距离保存到\下划线{o} RIGIN $ Y $。我们将Co-SNE施加到高维双曲生物数据以及无监督的双曲线表现。我们的结果表明，CO-SNE将高维色双曲数据降低到低维空间，而不会失去双曲线特性，显着优于PCA，T-SNE，UMAP和HOROPCA等流行可视化工具，其最后一个专门设计用于双曲数据。

双曲线空间可以连续嵌入分层结构。双曲神经网络（HNNS）通过将欧几里德特征提升到用于分类的双曲线空间来利用这种代表性，优于具有已知分层结构的数据集上的欧几里德神经网络（ENNS）。但是，HNNS低于标准基准测试，具有不明确的层次结构，极大地限制了HNNS的实际适用性。我们的主要洞察力是，由于将欧几里德特征连接到双曲线分类器的混合架构引起，HNNS对渐变较差的较差的普通分类性能。我们通过简单地在训练HNN时简单地剪切欧几里德特征幅度来提出有效的解决方案。我们的实验结果表明，剪辑的HNNS成为超级双曲分类器：它们不仅始终如一地优于位于分层数据上的HNN，而且在MNIST，CIFAR10，CIFAR100和ImageNet基准上与ENN一起举行，具有更好的对抗鲁棒性和分销外检测。

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.

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.

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.

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.

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.

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.