与常规知识蒸馏（KD）不同，自我KD允许网络在没有额外网络的任何指导的情况下向自身学习知识。本文提议从图像混合物（Mixskd）执行自我KD，将这两种技术集成到统一的框架中。 Mixskd相互蒸馏以图形和概率分布在随机的原始图像和它们的混合图像之间以有意义的方式。因此，它通过对混合图像进行监督信号进行建模来指导网络学习跨图像知识。此外，我们通过汇总多阶段功能图来构建一个自学老师网络，以提供软标签以监督骨干分类器，从而进一步提高自我增强的功效。图像分类和转移学习到对象检测和语义分割的实验表明，混合物KD优于其他最先进的自我KD和数据增强方法。该代码可在https://github.com/winycg/self-kd-lib上找到。

无教师的在线知识蒸馏（KD）旨在培训多个学生模型的合奏，并彼此提炼知识。尽管现有的在线KD方法实现了理想的性能，但它们通常专注于阶级概率作为核心知识类型，而忽略了宝贵的特征代表性信息。我们为在线KD提供了一个相互的对比学习（MCL）框架。 MCL的核心思想是以在线方式进行对比分布的相互交互和对比度分布的转移。我们的MCL可以汇总跨网络嵌入信息，并最大化两个网络之间的相互信息的下限。这使每个网络能够从他人那里学习额外的对比知识，从而提供更好的特征表示形式，从而提高视觉识别任务的性能。除最后一层外，我们还将MCL扩展到辅助特征细化模块辅助的几个中间层。这进一步增强了在线KD的表示能力。关于图像分类和转移学习到视觉识别任务的实验表明，MCL可以针对最新的在线KD方法带来一致的性能提高。优势表明，MCL可以指导网络生成更好的特征表示。我们的代码可在https://github.com/winycg/mcl上公开获取。

现有作品通常集中于减少架构冗余以加速图像分类，但忽略输入图像的空间冗余。本文提出了有效的图像分类管道来解决此问题。我们首先通过称为Anchornet的轻量级补丁提案网络在输入图像上查明任务感知区域。然后，我们将这些局部语义斑块的空间冗余量喂入一般分类网络。与Deep CNN的流行设计不同，我们旨在仔细设计无中间卷积桨的锚固板的接收场。这样可以确保从高级空间位置到特定输入图像补丁的确切映射。每个补丁的贡献是可以解释的。此外，AnchOrnet与任何下游架构兼容。 Imagenet上的实验结果表明，我们的方法优于SOTA动态推理方法，其推理成本较少。我们的代码可在https://github.com/winycg/anchornet上找到。

知识蒸馏（KD）是一个有效的框架，旨在将有意义的信息从大型老师转移到较小的学生。通常，KD通常涉及如何定义和转移知识。以前的KD方法通常着重于挖掘各种形式的知识，例如功能地图和精致信息。但是，知识源自主要监督任务，因此是高度特定于任务的。在自我监督的代表学习的最新成功中，我们提出了一项辅助自我实施的增强任务，以指导网络学习更多有意义的功能。因此，我们可以从KD的这项任务中得出软性自我实施的增强分布作为更丰富的黑暗知识。与以前的知识不同，此分布编码从监督和自我监督的特征学习中编码联合知识。除了知识探索之外，我们建议在各个隐藏层上附加几个辅助分支，以充分利用分层特征图。每个辅助分支都被指导学习自学的增强任务，并将这种分布从教师到学生提炼。总体而言，我们称我们的KD方法为等级自我实施的增强知识蒸馏（HSSAKD）。标准图像分类的实验表明，离线和在线HSSAKD都在KD领域达到了最先进的表现。对象检测的进一步转移实验进一步验证了HSSAKD可以指导网络学习更好的功能。该代码可在https://github.com/winycg/hsakd上找到。

知识蒸馏通常涉及如何有效地定义和转移知识从教师到学生。尽管最近的自我监督的对比知识取得了最佳表现，但迫使网络学习此类知识可能会损害对原始班级识别任务的表示。因此，我们采用替代性的自我监督的增强任务来指导网络学习原始识别任务和自我监督的辅助任务的共同分布。它被证明是一种更丰富的知识，可以提高表示能力而不会失去正常的分类能力。此外，以前的方法仅在最终层之间传递概率知识是不完整的。我们建议将几个辅助分类器附加到层次中间特征图中，以生成多样化的自我监督知识，并执行一对一的转移以彻底教授学生网络。我们的方法显着超过了先前的SOTA SSKD，CIFAR-100的平均改善为2.56 \％，并且在广泛使用的网络对上的Imagenet上有0.77 \％的提高。代码可在https://github.com/winycg/hsakd上找到。

Dataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models. It encapsulates the knowledge from a large dataset into a smaller synthetic dataset. A model trained on this smaller distilled dataset can attain comparable performance to a model trained on the original training dataset. However, the existing dataset distillation techniques mainly aim at achieving the best trade-off between resource usage efficiency and model utility. The security risks stemming from them have not been explored. This study performs the first backdoor attack against the models trained on the data distilled by dataset distillation models in the image domain. Concretely, we inject triggers into the synthetic data during the distillation procedure rather than during the model training stage, where all previous attacks are performed. We propose two types of backdoor attacks, namely NAIVEATTACK and DOORPING. NAIVEATTACK simply adds triggers to the raw data at the initial distillation phase, while DOORPING iteratively updates the triggers during the entire distillation procedure. We conduct extensive evaluations on multiple datasets, architectures, and dataset distillation techniques. Empirical evaluation shows that NAIVEATTACK achieves decent attack success rate (ASR) scores in some cases, while DOORPING reaches higher ASR scores (close to 1.0) in all cases. Furthermore, we conduct a comprehensive ablation study to analyze the factors that may affect the attack performance. Finally, we evaluate multiple defense mechanisms against our backdoor attacks and show that our attacks can practically circumvent these defense mechanisms.

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.

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.

Given the increasingly intricate forms of partial differential equations (PDEs) in physics and related fields, computationally solving PDEs without analytic solutions inevitably suffers from the trade-off between accuracy and efficiency. Recent advances in neural operators, a kind of mesh-independent neural-network-based PDE solvers, have suggested the dawn of overcoming this challenge. In this emerging direction, Koopman neural operator (KNO) is a representative demonstration and outperforms other state-of-the-art alternatives in terms of accuracy and efficiency. Here we present KoopmanLab, a self-contained and user-friendly PyTorch module of the Koopman neural operator family for solving partial differential equations. Beyond the original version of KNO, we develop multiple new variants of KNO based on different neural network architectures to improve the general applicability of our module. These variants are validated by mesh-independent and long-term prediction experiments implemented on representative PDEs (e.g., the Navier-Stokes equation and the Bateman-Burgers equation) and ERA5 (i.e., one of the largest high-resolution data sets of global-scale climate fields). These demonstrations suggest the potential of KoopmanLab to be considered in diverse applications of partial differential equations.

A recent study has shown a phenomenon called neural collapse in that the within-class means of features and the classifier weight vectors converge to the vertices of a simplex equiangular tight frame at the terminal phase of training for classification. In this paper, we explore the corresponding structures of the last-layer feature centers and classifiers in semantic segmentation. Based on our empirical and theoretical analysis, we point out that semantic segmentation naturally brings contextual correlation and imbalanced distribution among classes, which breaks the equiangular and maximally separated structure of neural collapse for both feature centers and classifiers. However, such a symmetric structure is beneficial to discrimination for the minor classes. To preserve these advantages, we introduce a regularizer on feature centers to encourage the network to learn features closer to the appealing structure in imbalanced semantic segmentation. Experimental results show that our method can bring significant improvements on both 2D and 3D semantic segmentation benchmarks. Moreover, our method ranks 1st and sets a new record (+6.8% mIoU) on the ScanNet200 test leaderboard. Code will be available at https://github.com/dvlab-research/Imbalanced-Learning.